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https://paperswithcode.com/paper/after-retrieval-before-generation-enhancing
|
2505.17118
| null | null |
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG
|
Retrieval-augmented generation (RAG) systems face critical challenges in balancing internal (parametric) and external (retrieved) knowledge, especially when these sources conflict or are unreliable. To analyze these scenarios comprehensively, we construct the Trustworthiness Response Dataset (TRD) with 36,266 questions spanning four RAG settings. We reveal that existing approaches address isolated scenarios-prioritizing one knowledge source, naively merging both, or refusing answers-but lack a unified framework to handle different real-world conditions simultaneously. Therefore, we propose the BRIDGE framework, which dynamically determines a comprehensive response strategy of large language models (LLMs). BRIDGE leverages an adaptive weighting mechanism named soft bias to guide knowledge collection, followed by a Maximum Soft-bias Decision Tree to evaluate knowledge and select optimal response strategies (trust internal/external knowledge, or refuse). Experiments show BRIDGE outperforms baselines by 5-15% in accuracy while maintaining balanced performance across all scenarios. Our work provides an effective solution for LLMs' trustworthy responses in real-world RAG applications.
| null |
https://arxiv.org/abs/2505.17118v1
|
https://arxiv.org/pdf/2505.17118v1.pdf
| null |
[
"Xinbang Dai",
"Huikang Hu",
"Yuncheng Hua",
"Jiaqi Li",
"Yongrui Chen",
"Rihui Jin",
"Nan Hu",
"Guilin Qi"
] |
[
"RAG",
"Retrieval-augmented Generation"
] | 2025-05-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
},
{
"code_snippet_url": null,
"description": "**BART** is a [denoising autoencoder](https://paperswithcode.com/method/denoising-autoencoder) for pretraining sequence-to-sequence models. It is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard [Transformer](https://paperswithcode.com/method/transformer)-based neural machine translation architecture. It uses a standard seq2seq/NMT architecture with a bidirectional encoder (like [BERT](https://paperswithcode.com/method/bert)) and a left-to-right decoder (like [GPT](https://paperswithcode.com/method/gpt)). This means the encoder's attention mask is fully visible, like BERT, and the decoder's attention mask is causal, like [GPT2](https://paperswithcode.com/method/gpt-2).",
"full_name": "BART",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "BART",
"source_title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
"source_url": "https://arxiv.org/abs/1910.13461v1"
},
{
"code_snippet_url": "",
"description": "**Retriever-Augmented Generation**, or **RAG**, is a type of language generation model that combines pre-trained parametric and non-parametric memory for language generation. Specifically, the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. For query $x$, Maximum Inner Product Search (MIPS) is used to find the top-K documents $z\\_{i}$. For final prediction $y$, we treat $z$ as a latent variable and marginalize over seq2seq predictions given different documents.",
"full_name": "RAG",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "RAG",
"source_title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks",
"source_url": "https://arxiv.org/abs/2005.11401v4"
}
] |
https://paperswithcode.com/paper/bridging-domain-generalization-to-multimodal
|
2507.03304
| null | null |
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations
|
Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation, learning strategies, and representation learning, have shown significant progress, they predominantly address single-modal data. With the emergence of numerous multi-modal datasets and increasing demand for multi-modal tasks, a key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set. Due to the inherent differences between modalities, directly transferring methods from single-modal DG to MMDG typically yields sub-optimal results. These methods often exhibit randomness during generalization due to the invisibility of target domains and fail to consider inter-modal consistency. Applying these methods independently to each modality in the MMDG setting before combining them can lead to divergent generalization directions across different modalities, resulting in degraded generalization capabilities. To address these challenges, we propose a novel approach that leverages Unified Representations to map different paired modalities together, effectively adapting DG methods to MMDG by enabling synchronized multi-modal improvements within the unified space. Additionally, we introduce a supervised disentanglement framework that separates modal-general and modal-specific information, further enhancing the alignment of unified representations. Extensive experiments on benchmark datasets, including EPIC-Kitchens and Human-Animal-Cartoon, demonstrate the effectiveness and superiority of our method in enhancing multi-modal domain generalization.
| null |
https://arxiv.org/abs/2507.03304v1
|
https://arxiv.org/pdf/2507.03304v1.pdf
| null |
[
"Hai Huang",
"Yan Xia",
"Sashuai Zhou",
"Hanting Wang",
"Shulei Wang",
"Zhou Zhao"
] |
[
"Disentanglement",
"Domain Generalization"
] | 2025-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/prompt-disentanglement-via-language-guidance
|
2507.02288
| null | null |
Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization
|
Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains. Notably, recent advances in pre-trained Visual Foundation Models (VFMs), such as CLIP, have demonstrated considerable potential in enhancing the generalization capabilities of deep learning models. Despite the increasing attention toward VFM-based domain prompt tuning within DG, the effective design of prompts capable of disentangling invariant features across diverse domains remains a critical challenge. In this paper, we propose addressing this challenge by leveraging the controllable and flexible language prompt of the VFM. Noting that the text modality of VFMs is naturally easier to disentangle, we introduce a novel framework for text feature-guided visual prompt tuning. This framework first automatically disentangles the text prompt using a large language model (LLM) and then learns domain-invariant visual representation guided by the disentangled text feature. However, relying solely on language to guide visual feature disentanglement has limitations, as visual features can sometimes be too complex or nuanced to be fully captured by descriptive text. To address this, we introduce Worst Explicit Representation Alignment (WERA), which extends text-guided visual prompts by incorporating an additional set of abstract prompts. These prompts enhance source domain diversity through stylized image augmentations, while alignment constraints ensure that visual representations remain consistent across both the original and augmented distributions. Experiments conducted on major DG datasets, including PACS, VLCS, OfficeHome, DomainNet, and TerraInc, demonstrate that our proposed method outperforms state-of-the-art DG methods.
| null |
https://arxiv.org/abs/2507.02288v1
|
https://arxiv.org/pdf/2507.02288v1.pdf
| null |
[
"De Cheng",
"Zhipeng Xu",
"Xinyang Jiang",
"Dongsheng Li",
"Nannan Wang",
"Xinbo Gao"
] |
[
"Descriptive",
"Disentanglement",
"Domain Generalization",
"Large Language Model",
"Visual Prompt Tuning"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
},
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/one-sample-is-enough-to-make-conformal
|
2506.16553
| null | null |
One Sample is Enough to Make Conformal Prediction Robust
|
Given any model, conformal prediction (CP) returns prediction sets guaranteed to include the true label with high adjustable probability. Robust CP (RCP) extends this to inputs with worst-case noise. A well-established approach is to use randomized smoothing for RCP since it is applicable to any black-box model and provides smaller sets compared to deterministic methods. However, current smoothing-based RCP requires many model forward passes per each input which is computationally expensive. We show that conformal prediction attains some robustness even with a forward pass on a single randomly perturbed input. Using any binary certificate we propose a single sample robust CP (RCP1). Our approach returns robust sets with smaller average set size compared to SOTA methods which use many (e.g. around 100) passes per input. Our key insight is to certify the conformal prediction procedure itself rather than individual scores. Our approach is agnostic to the setup (classification and regression). We further extend our approach to smoothing-based robust conformal risk control.
| null |
https://arxiv.org/abs/2506.16553v1
|
https://arxiv.org/pdf/2506.16553v1.pdf
| null |
[
"Soroush H. Zargarbashi",
"Mohammad Sadegh Akhondzadeh",
"Aleksandar Bojchevski"
] |
[
"Conformal Prediction",
"Prediction"
] | 2025-06-19T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Randomized Smoothing",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Robustness Methods",
"parent": null
},
"name": "Randomized Smoothing",
"source_title": "Certified Adversarial Robustness via Randomized Smoothing",
"source_url": "https://arxiv.org/abs/1902.02918v2"
},
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/mathoptai-jl-embed-trained-machine-learning
|
2507.03159
| null | null |
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
|
We present \texttt{MathOptAI.jl}, an open-source Julia library for embedding trained machine learning predictors into a JuMP model. \texttt{MathOptAI.jl} can embed a wide variety of neural networks, decision trees, and Gaussian Processes into a larger mathematical optimization model. In addition to interfacing a range of Julia-based machine learning libraries such as \texttt{Lux.jl} and \texttt{Flux.jl}, \texttt{MathOptAI.jl} uses Julia's Python interface to provide support for PyTorch models. When the PyTorch support is combined with \texttt{MathOptAI.jl}'s gray-box formulation, the function, Jacobian, and Hessian evaluations associated with the PyTorch model are offloaded to the GPU in Python, while the rest of the nonlinear oracles are evaluated on the CPU in Julia. \MathOptAI is available at https://github.com/lanl-ansi/MathOptAI.jl under a BSD-3 license.
|
We present \texttt{MathOptAI. jl}, an open-source Julia library for embedding trained machine learning predictors into a JuMP model.
|
https://arxiv.org/abs/2507.03159v1
|
https://arxiv.org/pdf/2507.03159v1.pdf
| null |
[
"Oscar Dowson",
"Robert B Parker",
"Russel Bent"
] |
[
"CPU",
"Gaussian Processes",
"GPU"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hita-holistic-tokenizer-for-autoregressive
|
2507.02358
| null | null |
Hita: Holistic Tokenizer for Autoregressive Image Generation
|
Vanilla autoregressive image generation models generate visual tokens step-by-step, limiting their ability to capture holistic relationships among token sequences. Moreover, because most visual tokenizers map local image patches into latent tokens, global information is limited. To address this, we introduce \textit{Hita}, a novel image tokenizer for autoregressive (AR) image generation. It introduces a holistic-to-local tokenization scheme with learnable holistic queries and local patch tokens. Hita incorporates two key strategies to better align with the AR generation process: 1) {arranging} a sequential structure with holistic tokens at the beginning, followed by patch-level tokens, and using causal attention to maintain awareness of previous tokens; and 2) adopting a lightweight fusion module before feeding the de-quantized tokens into the decoder to control information flow and prioritize holistic tokens. Extensive experiments show that Hita accelerates the training speed of AR generators and outperforms those trained with vanilla tokenizers, achieving \textbf{2.59 FID} and \textbf{281.9 IS} on the ImageNet benchmark. Detailed analysis of the holistic representation highlights its ability to capture global image properties, such as textures, materials, and shapes. Additionally, Hita also demonstrates effectiveness in zero-shot style transfer and image in-painting. The code is available at \href{https://github.com/CVMI-Lab/Hita}{https://github.com/CVMI-Lab/Hita}.
| null |
https://arxiv.org/abs/2507.02358v4
|
https://arxiv.org/pdf/2507.02358v4.pdf
| null |
[
"Anlin Zheng",
"Haochen Wang",
"Yucheng Zhao",
"Weipeng Deng",
"Tiancai Wang",
"Xiangyu Zhang",
"Xiaojuan Qi"
] |
[
"Image Generation",
"Style Transfer"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
},
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/rethinking-discrete-tokens-treating-them-as
|
2507.01756
| null | null |
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis
|
Recent advances in large language models (LLMs) have spurred interests in encoding images as discrete tokens and leveraging autoregressive (AR) frameworks for visual generation. However, the quantization process in AR-based visual generation models inherently introduces information loss that degrades image fidelity. To mitigate this limitation, recent studies have explored to autoregressively predict continuous tokens. Unlike discrete tokens that reside in a structured and bounded space, continuous representations exist in an unbounded, high-dimensional space, making density estimation more challenging and increasing the risk of generating out-of-distribution artifacts. Based on the above findings, this work introduces DisCon (Discrete-Conditioned Continuous Autoregressive Model), a novel framework that reinterprets discrete tokens as conditional signals rather than generation targets. By modeling the conditional probability of continuous representations conditioned on discrete tokens, DisCon circumvents the optimization challenges of continuous token modeling while avoiding the information loss caused by quantization. DisCon achieves a gFID score of 1.38 on ImageNet 256$\times$256 generation, outperforming state-of-the-art autoregressive approaches by a clear margin.
| null |
https://arxiv.org/abs/2507.01756v1
|
https://arxiv.org/pdf/2507.01756v1.pdf
| null |
[
"Peng Zheng",
"Junke Wang",
"Yi Chang",
"Yizhou Yu",
"Rui Ma",
"Zuxuan Wu"
] |
[
"Density Estimation",
"Image Generation",
"Quantization"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-training-free-style-personalization-via
|
2507.04482
| null | null |
A Training-Free Style-Personalization via Scale-wise Autoregressive Model
|
We present a training-free framework for style-personalized image generation that controls content and style information during inference using a scale-wise autoregressive model. Our method employs a three-path design--content, style, and generation--each guided by a corresponding text prompt, enabling flexible and efficient control over image semantics without any additional training. A central contribution of this work is a step-wise and attention-wise intervention analysis. Through systematic prompt and feature injection, we find that early-to-middle generation steps play a pivotal role in shaping both content and style, and that query features predominantly encode content-specific information. Guided by these insights, we introduce two targeted mechanisms: Key Stage Attention Sharing, which aligns content and style during the semantically critical steps, and Adaptive Query Sharing, which reinforces content semantics in later steps through similarity-aware query blending. Extensive experiments demonstrate that our method achieves competitive style fidelity and prompt fidelity compared to fine-tuned baselines, while offering faster inference and greater deployment flexibility.
| null |
https://arxiv.org/abs/2507.04482v1
|
https://arxiv.org/pdf/2507.04482v1.pdf
| null |
[
"Kyoungmin Lee",
"Jihun Park",
"Jongmin Gim",
"Wonhyeok Choi",
"Kyumin Hwang",
"Jaeyeul Kim",
"Sunghoon Im"
] |
[
"Image Generation",
"Personalized Image Generation"
] | 2025-07-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cyclevar-repurposing-autoregressive-model-for
|
2506.23347
| null | null |
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
|
The current conditional autoregressive image generation methods have shown promising results, yet their potential remains largely unexplored in the practical unsupervised image translation domain, which operates without explicit cross-domain correspondences. A critical limitation stems from the discrete quantization inherent in traditional Vector Quantization-based frameworks, which disrupts gradient flow between the Variational Autoencoder decoder and causal Transformer, impeding end-to-end optimization during adversarial training in image space. To tackle this issue, we propose using Softmax Relaxed Quantization, a novel approach that reformulates codebook selection as a continuous probability mixing process via Softmax, thereby preserving gradient propagation. Building upon this differentiable foundation, we introduce CycleVAR, which reformulates image-to-image translation as image-conditional visual autoregressive generation by injecting multi-scale source image tokens as contextual prompts, analogous to prefix-based conditioning in language models. CycleVAR exploits two modes to generate the target image tokens, including (1) serial multi-step generation, enabling iterative refinement across scales, and (2) parallel one-step generation synthesizing all resolution outputs in a single forward pass. Experimental findings indicate that the parallel one-step generation mode attains superior translation quality with quicker inference speed than the serial multi-step mode in unsupervised scenarios. Furthermore, both quantitative and qualitative results indicate that CycleVAR surpasses previous state-of-the-art unsupervised image translation models, \textit{e}.\textit{g}., CycleGAN-Turbo.
| null |
https://arxiv.org/abs/2506.23347v2
|
https://arxiv.org/pdf/2506.23347v2.pdf
| null |
[
"Yi Liu",
"Shengqian Li",
"Zuzeng Lin",
"Feng Wang",
"Si Liu"
] |
[
"Image Generation",
"Image-to-Image Translation",
"Quantization",
"Translation"
] | 2025-06-29T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/omni-video-democratizing-unified-video
|
2507.06119
| null | null |
Omni-Video: Democratizing Unified Video Understanding and Generation
|
Notable breakthroughs in unified understanding and generation modeling have led to remarkable advancements in image understanding, reasoning, production and editing, yet current foundational models predominantly focus on processing images, creating a gap in the development of unified models for video understanding and generation. This report presents Omni-Video, an efficient and effective unified framework for video understanding, generation, as well as instruction-based editing. Our key insight is to teach existing multimodal large language models (MLLMs) to produce continuous visual clues that are used as the input of diffusion decoders, which produce high-quality videos conditioned on these visual clues. To fully unlock the potential of our system for unified video modeling, we integrate several technical improvements: 1) a lightweight architectural design that respectively attaches a vision head on the top of MLLMs and a adapter before the input of diffusion decoders, the former produce visual tokens for the latter, which adapts these visual tokens to the conditional space of diffusion decoders; and 2) an efficient multi-stage training scheme that facilitates a fast connection between MLLMs and diffusion decoders with limited data and computational resources. We empirically demonstrate that our model exhibits satisfactory generalization abilities across video generation, editing and understanding tasks.
| null |
https://arxiv.org/abs/2507.06119v2
|
https://arxiv.org/pdf/2507.06119v2.pdf
| null |
[
"Zhiyu Tan",
"Hao Yang",
"Luozheng Qin",
"Jia Gong",
"Mengping Yang",
"Hao Li"
] |
[
"Video Generation",
"Video Understanding"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Adapter",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Adapter",
"source_title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing",
"source_url": "https://arxiv.org/abs/2101.03289v5"
},
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/cot-lized-diffusion-let-s-reinforce-t2i
|
2507.04451
| null | null |
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step
|
Current text-to-image (T2I) generation models struggle to align spatial composition with the input text, especially in complex scenes. Even layout-based approaches yield suboptimal spatial control, as their generation process is decoupled from layout planning, making it difficult to refine the layout during synthesis. We present CoT-Diff, a framework that brings step-by-step CoT-style reasoning into T2I generation by tightly integrating Multimodal Large Language Model (MLLM)-driven 3D layout planning with the diffusion process. CoT-Diff enables layout-aware reasoning inline within a single diffusion round: at each denoising step, the MLLM evaluates intermediate predictions, dynamically updates the 3D scene layout, and continuously guides the generation process. The updated layout is converted into semantic conditions and depth maps, which are fused into the diffusion model via a condition-aware attention mechanism, enabling precise spatial control and semantic injection. Experiments on 3D Scene benchmarks show that CoT-Diff significantly improves spatial alignment and compositional fidelity, and outperforms the state-of-the-art method by 34.7% in complex scene spatial accuracy, thereby validating the effectiveness of this entangled generation paradigm.
| null |
https://arxiv.org/abs/2507.04451v1
|
https://arxiv.org/pdf/2507.04451v1.pdf
| null |
[
"Zheyuan Liu",
"Munan Ning",
"Qihui Zhang",
"Shuo Yang",
"Zhongrui Wang",
"Yiwei Yang",
"Xianzhe Xu",
"Yibing Song",
"Weihua Chen",
"Fan Wang",
"Li Yuan"
] |
[
"Denoising",
"Large Language Model",
"Multimodal Large Language Model"
] | 2025-07-06T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
},
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/locality-aware-parallel-decoding-for
|
2507.01957
| null | null |
Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation
|
We present Locality-aware Parallel Decoding (LPD) to accelerate autoregressive image generation. Traditional autoregressive image generation relies on next-patch prediction, a memory-bound process that leads to high latency. Existing works have tried to parallelize next-patch prediction by shifting to multi-patch prediction to accelerate the process, but only achieved limited parallelization. To achieve high parallelization while maintaining generation quality, we introduce two key techniques: (1) Flexible Parallelized Autoregressive Modeling, a novel architecture that enables arbitrary generation ordering and degrees of parallelization. It uses learnable position query tokens to guide generation at target positions while ensuring mutual visibility among concurrently generated tokens for consistent parallel decoding. (2) Locality-aware Generation Ordering, a novel schedule that forms groups to minimize intra-group dependencies and maximize contextual support, enhancing generation quality. With these designs, we reduce the generation steps from 256 to 20 (256$\times$256 res.) and 1024 to 48 (512$\times$512 res.) without compromising quality on the ImageNet class-conditional generation, and achieving at least 3.4$\times$ lower latency than previous parallelized autoregressive models.
| null |
https://arxiv.org/abs/2507.01957v1
|
https://arxiv.org/pdf/2507.01957v1.pdf
| null |
[
"Zhuoyang Zhang",
"Luke J. Huang",
"Chengyue Wu",
"Shang Yang",
"Kelly Peng",
"Yao Lu",
"Song Han"
] |
[
"Image Generation",
"Prediction"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-automated-llm-speedrunning-benchmark
|
2506.22419
| null | null |
The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements
|
Rapid advancements in large language models (LLMs) have the potential to assist in scientific progress. A critical capability toward this endeavor is the ability to reproduce existing work. To evaluate the ability of AI agents to reproduce results in an active research area, we introduce the Automated LLM Speedrunning Benchmark, leveraging the research community contributions on the NanoGPT speedrun, a competition to train a GPT-2 model in the shortest time. Each of the 19 speedrun tasks provides the agent with the previous records training script, optionally paired with one of three hint formats, ranging from pseudocode to paper-like descriptions of the new records improvements. Records execute quickly by design and speedrun improvements encompass diverse code-level changes, ranging from high-level algorithmic advancements to hardware-aware optimizations. These features make the benchmark both accessible and realistic for the frontier problem of improving LLM training. We find that recent reasoning LLMs combined with SoTA scaffolds struggle to reimplement already-known innovations in our benchmark, even when given detailed hints. Our benchmark thus provides a simple, non-saturated measure of an LLMs ability to automate scientific reproduction, a necessary (but not sufficient) skill for an autonomous research agent.
| null |
https://arxiv.org/abs/2506.22419v2
|
https://arxiv.org/pdf/2506.22419v2.pdf
| null |
[
"Bingchen Zhao",
"Despoina Magka",
"Minqi Jiang",
"Xian Li",
"Roberta Raileanu",
"Tatiana Shavrina",
"Jean-Christophe Gagnon-Audet",
"Kelvin Niu",
"Shagun Sodhani",
"Michael Shvartsman",
"Andrei Lupu",
"Alisia Lupidi",
"Edan Toledo",
"Karen Hambardzumyan",
"Martin Josifoski",
"Thomas Foster",
"Lucia Cipolina-Kun",
"Abhishek Charnalia",
"Derek Dunfield",
"Alexander H. Miller",
"Oisin Mac Aodha",
"Jakob Foerster",
"Yoram Bachrach"
] |
[] | 2025-06-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!\r\n\r\n\r\n“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!",
"full_name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v5"
},
{
"code_snippet_url": "",
"description": "**GPT-2** is a [Transformer](https://paperswithcode.com/methods/category/transformers) architecture that was notable for its size (1.5 billion parameters) on its release. The model is pretrained on a [WebText dataset](https://paperswithcode.com/dataset/webtext) - text from 45 million website links. It largely follows the previous [GPT](https://paperswithcode.com/method/gpt) architecture with some modifications:\r\n\r\n- [Layer normalization](https://paperswithcode.com/method/layer-normalization) is moved to the input of each sub-block, similar to a\r\npre-activation residual network and an additional layer normalization was added after the final self-attention block. \r\n\r\n- A modified initialization which accounts for the accumulation on the residual path with model depth\r\nis used. Weights of residual layers are scaled at initialization by a factor of $1/\\sqrt{N}$ where $N$ is the number of residual layers. \r\n\r\n- The vocabulary is expanded to 50,257. The context size is expanded from 512 to 1024 tokens and\r\na larger batch size of 512 is used.",
"full_name": "GPT-2",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "GPT-2",
"source_title": "Language Models are Unsupervised Multitask Learners",
"source_url": "https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf"
},
{
"code_snippet_url": null,
"description": "An unsupervised approach for identifying Hierarchical Information Threads by analysing the network of related articles in a collection. In particular, HINT leverages article timestamps and the 5W1H questions to identify related articles about an event or discussion. HINT then constructs a network representation of the articles,\r\nand identify threads as strongly connected hierarchical network communities.",
"full_name": "Hierarchical Information Threading",
"introduced_year": 2000,
"main_collection": null,
"name": "HINT",
"source_title": "Effective Hierarchical Information Threading Using Network Community Detection",
"source_url": "https://link.springer.com/chapter/10.1007/978-3-031-28244-7_44"
}
] |
https://paperswithcode.com/paper/elucidating-and-endowing-the-diffusion
|
2506.21722
| null | null |
Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration
|
While diffusion models demonstrate strong generative capabilities in image restoration (IR) tasks, their complex architectures and iterative processes limit their practical application compared to mainstream reconstruction-based general ordinary IR networks. Existing approaches primarily focus on optimizing network architecture and diffusion paths but overlook the integration of the diffusion training paradigm within general ordinary IR frameworks. To address these challenges, this paper elucidates key principles for adapting the diffusion training paradigm to general IR training through systematic analysis of time-step dependencies, network hierarchies, noise-level relationships, and multi-restoration task correlations, proposing a new IR framework supported by diffusion-based training. To enable IR networks to simultaneously restore images and model generative representations, we introduce a series of regularization strategies that align diffusion objectives with IR tasks, improving generalization in single-task scenarios. Furthermore, recognizing that diffusion-based generation exerts varying influences across different IR tasks, we develop an incremental training paradigm and task-specific adaptors, further enhancing performance in multi-task unified IR. Experiments demonstrate that our method significantly improves the generalization of IR networks in single-task IR and achieves superior performance in multi-task unified IR. Notably, the proposed framework can be seamlessly integrated into existing general IR architectures.
| null |
https://arxiv.org/abs/2506.21722v1
|
https://arxiv.org/pdf/2506.21722v1.pdf
| null |
[
"Xin Lu",
"Xueyang Fu",
"Jie Xiao",
"Zihao Fan",
"Yurui Zhu",
"Zheng-Jun Zha"
] |
[
"Image Restoration"
] | 2025-06-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
},
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/ld-rps-zero-shot-unified-image-restoration
|
2507.00790
| null | null |
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
|
Unified image restoration is a significantly challenging task in low-level vision. Existing methods either make tailored designs for specific tasks, limiting their generalizability across various types of degradation, or rely on training with paired datasets, thereby suffering from closed-set constraints. To address these issues, we propose a novel, dataset-free, and unified approach through recurrent posterior sampling utilizing a pretrained latent diffusion model. Our method incorporates the multimodal understanding model to provide sematic priors for the generative model under a task-blind condition. Furthermore, it utilizes a lightweight module to align the degraded input with the generated preference of the diffusion model, and employs recurrent refinement for posterior sampling. Extensive experiments demonstrate that our method outperforms state-of-the-art methods, validating its effectiveness and robustness. Our code and data will be available at https://github.com/AMAP-ML/LD-RPS.
| null |
https://arxiv.org/abs/2507.00790v2
|
https://arxiv.org/pdf/2507.00790v2.pdf
| null |
[
"Huaqiu Li",
"Yong Wang",
"Tongwen Huang",
"Hailang Huang",
"Haoqian Wang",
"Xiangxiang Chu"
] |
[
"Image Restoration",
"Unified Image Restoration"
] | 2025-07-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
},
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/desta2-5-audio-toward-general-purpose-large
|
2507.02768
| null | null |
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
|
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following, without requiring task-specific audio instruction-tuning. Recent LALMs typically augment Large Language Models (LLMs) with auditory capabilities by training on large-scale, manually curated or LLM-synthesized audio-instruction datasets. However, these approaches have often suffered from the catastrophic forgetting of the LLM's original language abilities. To address this, we revisit the data construction pipeline and propose DeSTA, a self-generated cross-modal alignment strategy in which the backbone LLM generates its own training targets. This approach preserves the LLM's native language proficiency while establishing effective audio-text alignment, thereby enabling zero-shot generalization without task-specific tuning. Using DeSTA, we construct DeSTA-AQA5M, a large-scale, task-agnostic dataset containing 5 million training samples derived from 7,000 hours of audio spanning 50 diverse datasets, including speech, environmental sounds, and music. DeSTA2.5-Audio achieves state-of-the-art or competitive performance across a wide range of audio-language benchmarks, including Dynamic-SUPERB, MMAU, SAKURA, Speech-IFEval, and VoiceBench. Comprehensive comparative studies demonstrate that our self-generated strategy outperforms widely adopted data construction and training strategies in both auditory perception and instruction-following capabilities. Our findings underscore the importance of carefully designed data construction in LALM development and offer practical insights for building robust, general-purpose LALMs.
|
We introduce DeSTA2. 5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following, without requiring task-specific audio instruction-tuning.
|
https://arxiv.org/abs/2507.02768v1
|
https://arxiv.org/pdf/2507.02768v1.pdf
| null |
[
"Ke-Han Lu",
"Zhehuai Chen",
"Szu-Wei Fu",
"Chao-Han Huck Yang",
"Sung-Feng Huang",
"Chih-Kai Yang",
"Chee-En Yu",
"Chun-Wei Chen",
"Wei-Chih Chen",
"Chien-yu Huang",
"Yi-Cheng Lin",
"Yu-Xiang Lin",
"Chi-An Fu",
"Chun-Yi Kuan",
"Wenze Ren",
"Xuanjun Chen",
"Wei-Ping Huang",
"En-Pei Hu",
"Tzu-Quan Lin",
"Yuan-Kuei Wu",
"Kuan-Po Huang",
"Hsiao-Ying Huang",
"Huang-Cheng Chou",
"Kai-Wei Chang",
"Cheng-Han Chiang",
"Boris Ginsburg",
"Yu-Chiang Frank Wang",
"Hung-Yi Lee"
] |
[
"cross-modal alignment",
"Instruction Following",
"Language Modeling",
"Language Modelling",
"Zero-shot Generalization"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/revisiting-llms-as-zero-shot-time-series
|
2506.00457
| null | null |
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
|
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecasting. While LLMs have shown potential in zero-shot forecasting through prompting alone, recent studies suggest that LLMs lack inherent effectiveness in forecasting. Given these conflicting findings, a rigorous validation is essential for drawing reliable conclusions. In this paper, we evaluate the effectiveness of LLMs as zero-shot forecasters compared to state-of-the-art domain-specific models. Our experiments show that LLM-based zero-shot forecasters often struggle to achieve high accuracy due to their sensitivity to noise, underperforming even simple domain-specific models. We have explored solutions to reduce LLMs' sensitivity to noise in the zero-shot setting, but improving their robustness remains a significant challenge. Our findings suggest that rather than emphasizing zero-shot forecasting, a more promising direction would be to focus on fine-tuning LLMs to better process numerical sequences. Our experimental code is available at https://github.com/junwoopark92/revisiting-LLMs-zeroshot-forecaster.
| null |
https://arxiv.org/abs/2506.00457v1
|
https://arxiv.org/pdf/2506.00457v1.pdf
| null |
[
"Junwoo Park",
"Hyuck Lee",
"Dohyun Lee",
"Daehoon Gwak",
"Jaegul Choo"
] |
[
"Sensitivity",
"Time Series",
"Time Series Forecasting"
] | 2025-05-31T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/2505-24
|
2505.24
| null | null | null | null | null |
https://arxiv.org/abs/2505.24
| null | null |
[] |
[] | null | null | null | null | null |
[] |
https://paperswithcode.com/paper/safe-pruning-lora-robust-distance-guided
|
2506.18931
| null | null |
Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
|
Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) enhances adaptability while reducing computational costs. However, fine-tuning can compromise safety alignment, even with benign data, increasing susceptibility to harmful outputs. Existing safety alignment methods struggle to capture complex parameter shifts, leading to suboptimal safety-utility trade-offs. To address this issue, we propose Safe Pruning LoRA (SPLoRA), a novel pruning-based approach that selectively removes LoRA layers that weaken safety alignment, improving safety while preserving performance. At its core, we introduce Empirical-DIEM (E-DIEM), a dimension-insensitive similarity metric that effectively detects safety misalignment in LoRA-adapted models. We conduct extensive experiments on LLMs fine-tuned with mixed of benign and malicious data, and purely benign datasets, evaluating SPLoRA across utility, safety, and reliability metrics. Results demonstrate that SPLoRA outperforms state-of-the-art safety alignment techniques, significantly reducing safety risks while maintaining or improving model performance and reliability. Additionally, SPLoRA reduces inference overhead, making it a scalable and efficient solution for deploying safer and more reliable LLMs. The code is available at https://github.com/AoShuang92/SPLoRA.
| null |
https://arxiv.org/abs/2506.18931v1
|
https://arxiv.org/pdf/2506.18931v1.pdf
| null |
[
"Shuang Ao",
"Yi Dong",
"Jinwei Hu",
"Sarvapali Ramchurn"
] |
[
"Safety Alignment"
] | 2025-06-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
}
] |
https://paperswithcode.com/paper/why-multi-interest-fairness-matters
|
2507.02000
| null | null |
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
|
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel framework, Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyFairCRS), aiming to promote multi-interest diversity fairness in dynamic and interactive Conversational Recommender Systems (CRSs). HyFairCRS first captures a wide range of user interests by establishing diverse hypergraphs through contrastive learning. These interests are then utilized in conversations to generate informative responses and ensure fair item predictions within the dynamic user-system feedback loop. Experiments on two CRS-based datasets show that HyFairCRS achieves a new state-of-the-art performance while effectively alleviating unfairness. Our code is available at https://github.com/zysensmile/HyFairCRS.
| null |
https://arxiv.org/abs/2507.02000v1
|
https://arxiv.org/pdf/2507.02000v1.pdf
| null |
[
"Yongsen Zheng",
"Zongxuan Xie",
"Guohua Wang",
"Ziyao Liu",
"Liang Lin",
"Kwok-Yan Lam"
] |
[
"Contrastive Learning",
"Fairness",
"Recommendation Systems"
] | 2025-07-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/d-fusion-direct-preference-optimization-for
|
2505.22002
| null | null |
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples
|
The practical applications of diffusion models have been limited by the misalignment between generated images and corresponding text prompts. Recent studies have introduced direct preference optimization (DPO) to enhance the alignment of these models. However, the effectiveness of DPO is constrained by the issue of visual inconsistency, where the significant visual disparity between well-aligned and poorly-aligned images prevents diffusion models from identifying which factors contribute positively to alignment during fine-tuning. To address this issue, this paper introduces D-Fusion, a method to construct DPO-trainable visually consistent samples. On one hand, by performing mask-guided self-attention fusion, the resulting images are not only well-aligned, but also visually consistent with given poorly-aligned images. On the other hand, D-Fusion can retain the denoising trajectories of the resulting images, which are essential for DPO training. Extensive experiments demonstrate the effectiveness of D-Fusion in improving prompt-image alignment when applied to different reinforcement learning algorithms.
| null |
https://arxiv.org/abs/2505.22002v1
|
https://arxiv.org/pdf/2505.22002v1.pdf
| null |
[
"Zijing Hu",
"Fengda Zhang",
"Kun Kuang"
] |
[
"Denoising"
] | 2025-05-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Direct Preference Optimization",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Offline Reinforcement Learning Methods",
"parent": null
},
"name": "DPO",
"source_title": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model",
"source_url": "https://arxiv.org/abs/2305.18290v3"
},
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/advancing-learnable-multi-agent-pathfinding
|
2506.23793
| null | null |
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
|
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment. While solving MAPF optimally has been proven to be NP-hard, scalable, and efficient, solvers are vital for real-world applications like logistics, search-and-rescue, etc. To this end, decentralized suboptimal MAPF solvers that leverage machine learning have come on stage. Building on the success of the recently introduced MAPF-GPT, a pure imitation learning solver, we introduce MAPF-GPT-DDG. This novel approach effectively fine-tunes the pre-trained MAPF model using centralized expert data. Leveraging a novel delta-data generation mechanism, MAPF-GPT-DDG accelerates training while significantly improving performance at test time. Our experiments demonstrate that MAPF-GPT-DDG surpasses all existing learning-based MAPF solvers, including the original MAPF-GPT, regarding solution quality across many testing scenarios. Remarkably, it can work with MAPF instances involving up to 1 million agents in a single environment, setting a new milestone for scalability in MAPF domains.
|
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment.
|
https://arxiv.org/abs/2506.23793v1
|
https://arxiv.org/pdf/2506.23793v1.pdf
| null |
[
"Anton Andreychuk",
"Konstantin Yakovlev",
"Aleksandr Panov",
"Alexey Skrynnik"
] |
[
"Imitation Learning",
"Trajectory Planning"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/retrospective-memory-for-camouflaged-object
|
2506.15244
| null | null |
Retrospective Memory for Camouflaged Object Detection
|
Camouflaged object detection (COD) primarily focuses on learning subtle yet discriminative representations from complex scenes. Existing methods predominantly follow the parametric feedforward architecture based on static visual representation modeling. However, they lack explicit mechanisms for acquiring historical context, limiting their adaptation and effectiveness in handling challenging camouflage scenes. In this paper, we propose a recall-augmented COD architecture, namely RetroMem, which dynamically modulates camouflage pattern perception and inference by integrating relevant historical knowledge into the process. Specifically, RetroMem employs a two-stage training paradigm consisting of a learning stage and a recall stage to construct, update, and utilize memory representations effectively. During the learning stage, we design a dense multi-scale adapter (DMA) to improve the pretrained encoder's capability to capture rich multi-scale visual information with very few trainable parameters, thereby providing foundational inferences. In the recall stage, we propose a dynamic memory mechanism (DMM) and an inference pattern reconstruction (IPR). These components fully leverage the latent relationships between learned knowledge and current sample context to reconstruct the inference of camouflage patterns, thereby significantly improving the model's understanding of camouflage scenes. Extensive experiments on several widely used datasets demonstrate that our RetroMem significantly outperforms existing state-of-the-art methods.
| null |
https://arxiv.org/abs/2506.15244v1
|
https://arxiv.org/pdf/2506.15244v1.pdf
| null |
[
"Chenxi Zhang",
"Jiayun Wu",
"Qing Zhang",
"Yazhe Zhai",
"Youwei Pang"
] |
[
"Object",
"object-detection",
"Object Detection"
] | 2025-06-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Adapter",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Adapter",
"source_title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing",
"source_url": "https://arxiv.org/abs/2101.03289v5"
}
] |
https://paperswithcode.com/paper/sub-moe-efficient-mixture-of-expert-llms
|
2506.23266
| null | null |
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
|
Mixture of Experts (MoE) LLMs face significant obstacles due to their massive parameter scale, which imposes memory, storage, and deployment challenges. Although recent expert merging methods promise greater efficiency by consolidating multiple experts, they are fundamentally hindered by parameter conflicts arising from expert specialization. In this paper, we present Sub-MoE, a novel MoE compression framework via Subspace Expert Merging. Our key insight is to perform joint Singular Value Decomposition (SVD) on concatenated expert weights, reducing conflicting parameters by extracting shared $U$-matrices while enabling effective merging of the expert-specific $V$ components. Specifically, Sub-MoE consists of two innovative phases: (1) Adaptive Expert Clustering, which groups functionally coherent experts via K-means clustering based on cosine similarity of expert outputs; and (2) Subspace Expert Merging, which first enforces Experts Union Decomposition to derive the shared $U$-matrix across experts in the same group, then pursues frequency-based merging for individual $V$-matrices, and finalizes expert reconstruction using the merged $V$-matrix. In this way, we align and fuse experts in a shared subspace, and can be extended with intra-expert compression for further inference optimization. Extensive experiments on Mixtral, DeepSeek, and Qwen-1.5|3 MoE LLMs demonstrate that our Sub-MoE significantly outperforms existing expert pruning and merging methods. Notably, our Sub-MoE maintains 96\%|86\% of original performance with 25\%|50\% expert reduction on Mixtral-8x7B in zero-shot benchmarks. Code will be released at https://github.com/lliai/MoERazor.
| null |
https://arxiv.org/abs/2506.23266v1
|
https://arxiv.org/pdf/2506.23266v1.pdf
| null |
[
"Lujun Li",
"Zhu Qiyuan",
"Jiacheng Wang",
"Wei Li",
"Hao Gu",
"Sirui Han",
"Yike Guo"
] |
[
"Inference Optimization",
"Mixture-of-Experts"
] | 2025-06-29T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Mixture of Experts",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Ensembling",
"parent": null
},
"name": "MoE",
"source_title": "Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs",
"source_url": "https://arxiv.org/abs/2403.07743v3"
},
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
},
{
"code_snippet_url": "https://cryptoabout.info",
"description": "**k-Means Clustering** is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\\mu\\left(1\\right),\\ldots,\\mu\\left(k\\right)$} to different values, then alternating between two steps until convergence:\r\n\r\n(i) each training example is assigned to cluster $i$ where $i$ is the index of the nearest centroid $\\mu^{(i)}$\r\n\r\n(ii) each centroid $\\mu^{(i)}$ is updated to the mean of all training examples $x^{(j)}$ assigned to cluster $i$.\r\n\r\nText Source: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [scikit-learn](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html)",
"full_name": "k-Means Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "k-Means Clustering",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
}
] |
https://paperswithcode.com/paper/graft-integrating-the-domain-knowledge-via
|
2506.23940
| null | null |
Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs
|
Multimodal Large Language Models (MLLMs) have achieved success across various domains. However, their applicability tends to degrade when confronted with different types of data inputs, especially for MLLMs that have been fine-tuned for specific tasks. Despite its importance, the study of knowledge sharing among domain-specific MLLMs--such as those trained for mathematics or code--remains largely underexplored. To address the fragmentation of knowledge across domain-specialized MLLMs, we propose a unified parameter integration framework that enables modular composition of expert capabilities. Our method is grounded in a novel Compatibility-Aware Parameter Splicing (CAPS) strategy, which leverages both local functional attribution and global information-theoretic signals to guide selective parameter fusion. By extending this mechanism to the low-rank adaptation layer granularity, we ensure efficient integration with minimal inference overhead. Furthermore, we introduce a domain compatibility scoring mechanism that quantifies inter-expert alignment at the activation level and correlates with downstream task utility. This principled fusion protocol allows the final model to synergize heterogeneous expertise while preserving structural modularity. Extensive evaluations across diverse multimodal benchmarks validate the effectiveness of our framework, offering a scalable path toward compositional, domain-adaptive MLLMs.
| null |
https://arxiv.org/abs/2506.23940v2
|
https://arxiv.org/pdf/2506.23940v2.pdf
| null |
[
"Yang Dai",
"Jianxiang An",
"Tianwei Lin",
"Hongyang He",
"Hongzhe Huang",
"Wenqiao Zhang",
"Zheqi Lv",
"Siliang Tang",
"Yueting Zhuang"
] |
[] | 2025-06-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Given a pattern $P,$ that is more complicated than the patterns, we fragment $P$ into simpler patterns such that their exact count is known. In the subgraph GNN proposed earlier, look into the subgraph of the host graph. We have seen that this technique is scalable on large graphs. Also, we have seen that subgraph GNN is more expressive and efficient than traditional GNN. So, we tried to explore the expressibility when the pattern is fragmented into smaller subpatterns.",
"full_name": "Fragmentation",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Localization Models",
"parent": null
},
"name": "Fragmentation",
"source_title": "Improving Expressivity of Graph Neural Networks using Localization",
"source_url": "https://arxiv.org/abs/2305.19659v3"
}
] |
https://paperswithcode.com/paper/generative-representational-learning-of
|
2506.11999
| null | null |
Generative Representational Learning of Foundation Models for Recommendation
|
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexities of multi-task learning, including knowledge sharing & conflict resolution, and convergence speed inconsistencies. To address these limitations, we introduce RecFound, a generative representational learning framework for recommendation foundation models. We construct the first comprehensive dataset for recommendation foundation models covering both generative and embedding tasks across diverse scenarios. Based on this dataset, we propose a novel multi-task training scheme featuring a Task-wise Mixture of Low-rank Experts (TMoLE) to handle knowledge sharing & conflict, a Step-wise Convergence-oriented Sample Scheduler (S2Sched) to address inconsistent convergence, and a Model Merge module to balance the performance across tasks. Experiments demonstrate that RecFound achieves state-of-the-art performance across various recommendation tasks, outperforming existing baselines.
| null |
https://arxiv.org/abs/2506.11999v3
|
https://arxiv.org/pdf/2506.11999v3.pdf
| null |
[
"Zheli Zhou",
"Chenxu Zhu",
"Jianghao Lin",
"Bo Chen",
"Ruiming Tang",
"Weinan Zhang",
"Yong Yu"
] |
[
"Multi-Task Learning",
"Recommendation Systems"
] | 2025-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/datasets-for-fairness-in-language-models-an
|
2506.23411
| null | null |
Datasets for Fairness in Language Models: An In-Depth Survey
|
Fairness benchmarks play a central role in shaping how we evaluate language models, yet surprisingly little attention has been given to examining the datasets that these benchmarks rely on. This survey addresses that gap by presenting a broad and careful review of the most widely used fairness datasets in current language model research, characterizing them along several key dimensions including their origin, scope, content, and intended use to help researchers better appreciate the assumptions and limitations embedded in these resources. To support more meaningful comparisons and analyses, we introduce a unified evaluation framework that reveals consistent patterns of demographic disparities across datasets and scoring methods. Applying this framework to twenty four common benchmarks, we highlight the often overlooked biases that can influence conclusions about model fairness and offer practical guidance for selecting, combining, and interpreting these datasets. We also point to opportunities for creating new fairness benchmarks that reflect more diverse social contexts and encourage more thoughtful use of these tools going forward. All code, data, and detailed results are publicly available at https://github.com/vanbanTruong/Fairness-in-Large-Language-Models/tree/main/datasets to promote transparency and reproducibility across the research community.
| null |
https://arxiv.org/abs/2506.23411v1
|
https://arxiv.org/pdf/2506.23411v1.pdf
| null |
[
"Jiale Zhang",
"Zichong Wang",
"Avash Palikhe",
"Zhipeng Yin",
"Wenbin Zhang"
] |
[
"Fairness"
] | 2025-06-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dynamic-graph-condensation
|
2506.13099
| null | null |
Dynamic Graph Condensation
|
Recent research on deep graph learning has shifted from static to dynamic graphs, motivated by the evolving behaviors observed in complex real-world systems. However, the temporal extension in dynamic graphs poses significant data efficiency challenges, including increased data volume, high spatiotemporal redundancy, and reliance on costly dynamic graph neural networks (DGNNs). To alleviate the concerns, we pioneer the study of dynamic graph condensation (DGC), which aims to substantially reduce the scale of dynamic graphs for data-efficient DGNN training. Accordingly, we propose DyGC, a novel framework that condenses the real dynamic graph into a compact version while faithfully preserving the inherent spatiotemporal characteristics. Specifically, to endow synthetic graphs with realistic evolving structures, a novel spiking structure generation mechanism is introduced. It draws on the dynamic behavior of spiking neurons to model temporally-aware connectivity in dynamic graphs. Given the tightly coupled spatiotemporal dependencies, DyGC proposes a tailored distribution matching approach that first constructs a semantically rich state evolving field for dynamic graphs, and then performs fine-grained spatiotemporal state alignment to guide the optimization of the condensed graph. Experiments across multiple dynamic graph datasets and representative DGNN architectures demonstrate the effectiveness of DyGC. Notably, our method retains up to 96.2% DGNN performance with only 0.5% of the original graph size, and achieves up to 1846 times training speedup.
| null |
https://arxiv.org/abs/2506.13099v1
|
https://arxiv.org/pdf/2506.13099v1.pdf
| null |
[
"Dong Chen",
"Shuai Zheng",
"Yeyu Yan",
"Muhao Xu",
"Zhenfeng Zhu",
"Yao Zhao",
"Kunlun He"
] |
[
"Graph Learning"
] | 2025-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/simple-yet-effective-graph-distillation-via
|
2505.20807
| null | null |
Simple yet Effective Graph Distillation via Clustering
|
Despite plentiful successes achieved by graph representation learning in various domains, the training of graph neural networks (GNNs) still remains tenaciously challenging due to the tremendous computational overhead needed for sizable graphs in practice. Recently, graph data distillation (GDD), which seeks to distill large graphs into compact and informative ones, has emerged as a promising technique to enable efficient GNN training. However, most existing GDD works rely on heuristics that align model gradients or representation distributions on condensed and original graphs, leading to compromised result quality, expensive training for distilling large graphs, or both. Motivated by this, this paper presents an efficient and effective GDD approach, ClustGDD. Under the hood, ClustGDD resorts to synthesizing the condensed graph and node attributes through fast and theoretically-grounded clustering that minimizes the within-cluster sum of squares and maximizes the homophily on the original graph. The fundamental idea is inspired by our empirical and theoretical findings unveiling the connection between clustering and empirical condensation quality using Fr\'echet Inception Distance, a well-known quality metric for synthetic images. Furthermore, to mitigate the adverse effects caused by the homophily-based clustering, ClustGDD refines the nodal attributes of the condensed graph with a small augmentation learned via class-aware graph sampling and consistency loss. Our extensive experiments exhibit that GNNs trained over condensed graphs output by ClustGDD consistently achieve superior or comparable performance to state-of-the-art GDD methods in terms of node classification on five benchmark datasets, while being orders of magnitude faster.
| null |
https://arxiv.org/abs/2505.20807v1
|
https://arxiv.org/pdf/2505.20807v1.pdf
| null |
[
"Yurui Lai",
"Taiyan Zhang",
"Renchi Yang"
] |
[
"Clustering",
"Graph Representation Learning",
"Graph Sampling",
"Node Classification"
] | 2025-05-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss (formulated as normalized softmax) that pushes the embeddings of the matched image-text pair together and pushing those of non-matched image-text pair apart. The model learns to align visual and language representations of the image and text pairs using the contrastive loss. The representations can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers zero-shot visual classification and cross-modal search including image-to-text search, text-to image search and even search with joint image+text queries.",
"full_name": "ALIGN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "Involves models that adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks like visual question answering and visual captioning.\r\n\r\nAccording to [Du et al. (2022)](https://arxiv.org/pdf/2202.10936.pdf), information coming from the different modalities can be encoded in three ways: fusion encoder, dual encoder, and a combination of both. \r\n\r\nReferences:\r\n\r\n- [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/pdf/2202.10936.pdf)\r\n- [Vision Language models: towards multi-modal deep learning](https://theaisummer.com/vision-language-models/)",
"name": "Vision and Language Pre-Trained Models",
"parent": null
},
"name": "ALIGN",
"source_title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
"source_url": "https://arxiv.org/abs/2102.05918v2"
}
] |
https://paperswithcode.com/paper/revisiting-cropa-a-reproducibility-study-and
|
2506.22982
| null | null |
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
|
Large Vision-Language Models (VLMs) have revolutionized computer vision, enabling tasks such as image classification, captioning, and visual question answering. However, they remain highly vulnerable to adversarial attacks, particularly in scenarios where both visual and textual modalities can be manipulated. In this study, we conduct a comprehensive reproducibility study of "An Image is Worth 1000 Lies: Adversarial Transferability Across Prompts on Vision-Language Models" validating the Cross-Prompt Attack (CroPA) and confirming its superior cross-prompt transferability compared to existing baselines. Beyond replication we propose several key improvements: (1) A novel initialization strategy that significantly improves Attack Success Rate (ASR). (2) Investigate cross-image transferability by learning universal perturbations. (3) A novel loss function targeting vision encoder attention mechanisms to improve generalization. Our evaluation across prominent VLMs -- including Flamingo, BLIP-2, and InstructBLIP as well as extended experiments on LLaVA validates the original results and demonstrates that our improvements consistently boost adversarial effectiveness. Our work reinforces the importance of studying adversarial vulnerabilities in VLMs and provides a more robust framework for generating transferable adversarial examples, with significant implications for understanding the security of VLMs in real-world applications.
| null |
https://arxiv.org/abs/2506.22982v1
|
https://arxiv.org/pdf/2506.22982v1.pdf
| null |
[
"Atharv Mittal",
"Agam Pandey",
"Amritanshu Tiwari",
"Sukrit Jindal",
"Swadesh Swain"
] |
[
"image-classification",
"Image Classification",
"Question Answering",
"Visual Question Answering"
] | 2025-06-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cavalry-v-a-large-scale-generator-framework
|
2507.00817
| null | null |
CAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs
|
Video Multimodal Large Language Models (V-MLLMs) have shown impressive capabilities in temporal reasoning and cross-modal understanding, yet their vulnerability to adversarial attacks remains underexplored due to unique challenges: complex cross-modal reasoning mechanisms, temporal dependencies, and computational constraints. We present CAVALRY-V (Cross-modal Language-Vision Adversarial Yielding for Videos), a novel framework that directly targets the critical interface between visual perception and language generation in V-MLLMs. Our approach introduces two key innovations: (1) a dual-objective semantic-visual loss function that simultaneously disrupts the model's text generation logits and visual representations to undermine cross-modal integration, and (2) a computationally efficient two-stage generator framework that combines large-scale pre-training for cross-model transferability with specialized fine-tuning for spatiotemporal coherence. Empirical evaluation on comprehensive video understanding benchmarks demonstrates that CAVALRY-V significantly outperforms existing attack methods, achieving 22.8% average improvement over the best baseline attacks on both commercial systems (GPT-4.1, Gemini 2.0) and open-source models (QwenVL-2.5, InternVL-2.5, Llava-Video, Aria, MiniCPM-o-2.6). Our framework achieves flexibility through implicit temporal coherence modeling rather than explicit regularization, enabling significant performance improvements even on image understanding (34.4% average gain). This capability demonstrates CAVALRY-V's potential as a foundational approach for adversarial research across multimodal systems.
| null |
https://arxiv.org/abs/2507.00817v1
|
https://arxiv.org/pdf/2507.00817v1.pdf
| null |
[
"Jiaming Zhang",
"Rui Hu",
"Qing Guo",
"Wei Yang Bryan Lim"
] |
[
"Text Generation",
"Video Understanding"
] | 2025-07-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/visual-contextual-attack-jailbreaking-mllms
|
2507.02844
| null | null |
Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection
|
With the emergence of strong visual-language capabilities, multimodal large language models (MLLMs) have demonstrated tremendous potential for real-world applications. However, the security vulnerabilities exhibited by the visual modality pose significant challenges to deploying such models in open-world environments. Recent studies have successfully induced harmful responses from target MLLMs by encoding harmful textual semantics directly into visual inputs. However, in these approaches, the visual modality primarily serves as a trigger for unsafe behavior, often exhibiting semantic ambiguity and lacking grounding in realistic scenarios. In this work, we define a novel setting: visual-centric jailbreak, where visual information serves as a necessary component in constructing a complete and realistic jailbreak context. Building on this setting, we propose the VisCo (Visual Contextual) Attack. VisCo fabricates contextual dialogue using four distinct visual-focused strategies, dynamically generating auxiliary images when necessary to construct a visual-centric jailbreak scenario. To maximize attack effectiveness, it incorporates automatic toxicity obfuscation and semantic refinement to produce a final attack prompt that reliably triggers harmful responses from the target black-box MLLMs. Specifically, VisCo achieves a toxicity score of 4.78 and an Attack Success Rate (ASR) of 85% on MM-SafetyBench against GPT-4o, significantly outperforming the baseline, which performs a toxicity score of 2.48 and an ASR of 22.2%. The code is available at https://github.com/Dtc7w3PQ/Visco-Attack.
| null |
https://arxiv.org/abs/2507.02844v1
|
https://arxiv.org/pdf/2507.02844v1.pdf
| null |
[
"Ziqi Miao",
"Yi Ding",
"Lijun Li",
"Jing Shao"
] |
[] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robustereo-robust-zero-shot-stereo-matching
|
2507.01653
| null | null |
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
|
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative features from degraded images. These limitations significantly hinder zero-shot generalization to out-of-distribution weather conditions. In this paper, we propose \textbf{RobuSTereo}, a novel framework that enhances the zero-shot generalization of stereo matching models under adverse weather by addressing both data scarcity and feature extraction challenges. First, we introduce a diffusion-based simulation pipeline with a stereo consistency module, which generates high-quality stereo data tailored for adverse conditions. By training stereo matching models on our synthetic datasets, we reduce the domain gap between clean and degraded images, significantly improving the models' robustness to unseen weather conditions. The stereo consistency module ensures structural alignment across synthesized image pairs, preserving geometric integrity and enhancing depth estimation accuracy. Second, we design a robust feature encoder that combines a specialized ConvNet with a denoising transformer to extract stable and reliable features from degraded images. The ConvNet captures fine-grained local structures, while the denoising transformer refines global representations, effectively mitigating the impact of noise, low visibility, and weather-induced distortions. This enables more accurate disparity estimation even under challenging visual conditions. Extensive experiments demonstrate that \textbf{RobuSTereo} significantly improves the robustness and generalization of stereo matching models across diverse adverse weather scenarios.
| null |
https://arxiv.org/abs/2507.01653v1
|
https://arxiv.org/pdf/2507.01653v1.pdf
| null |
[
"Yuran Wang",
"Yingping Liang",
"Yutao Hu",
"Ying Fu"
] |
[
"Denoising",
"Depth Estimation",
"Disparity Estimation",
"Stereo Matching",
"Zero-shot Generalization"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gbgc-efficient-and-adaptive-graph-coarsening
|
2506.19224
| null | null |
GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing
|
The objective of graph coarsening is to generate smaller, more manageable graphs while preserving key information of the original graph. Previous work were mainly based on the perspective of spectrum-preserving, using some predefined coarsening rules to make the eigenvalues of the Laplacian matrix of the original graph and the coarsened graph match as much as possible. However, they largely overlooked the fact that the original graph is composed of subregions at different levels of granularity, where highly connected and similar nodes should be more inclined to be aggregated together as nodes in the coarsened graph. By combining the multi-granularity characteristics of the graph structure, we can generate coarsened graph at the optimal granularity. To this end, inspired by the application of granular-ball computing in multi-granularity, we propose a new multi-granularity, efficient, and adaptive coarsening method via granular-ball (GBGC), which significantly improves the coarsening results and efficiency. Specifically, GBGC introduces an adaptive granular-ball graph refinement mechanism, which adaptively splits the original graph from coarse to fine into granular-balls of different sizes and optimal granularity, and constructs the coarsened graph using these granular-balls as supernodes. In addition, compared with other state-of-the-art graph coarsening methods, the processing speed of this method can be increased by tens to hundreds of times and has lower time complexity. The accuracy of GBGC is almost always higher than that of the original graph due to the good robustness and generalization of the granular-ball computing, so it has the potential to become a standard graph data preprocessing method.
| null |
https://arxiv.org/abs/2506.19224v1
|
https://arxiv.org/pdf/2506.19224v1.pdf
| null |
[
"Shuyin Xia",
"Guan Wang",
"Gaojie Xu",
"Sen Zhao",
"Guoyin Wang"
] |
[] | 2025-06-24T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/flash-vstream-efficient-real-time
|
2506.23825
| null | null |
Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
|
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding. However, the understanding of long videos is still challenging, as their long-context nature results in significant computational and memory overhead. Most existing work treats long videos in the same way as short videos, which is inefficient for real-world applications and hard to generalize to even longer videos. To address these issues, we propose Flash-VStream, an efficient video language model capable of processing extremely long videos and responding to user queries in real time. Particularly, we design a Flash Memory module, containing a low-capacity context memory to aggregate long-context temporal information and model the distribution of information density, and a high-capacity augmentation memory to retrieve detailed spatial information based on this distribution. Compared to existing models, Flash-VStream achieves significant reductions in inference latency. Extensive experiments on long video benchmarks and comprehensive video benchmarks, i.e., EgoSchema, MLVU, LVBench, MVBench and Video-MME, demonstrate the state-of-the-art performance and outstanding efficiency of our method. Code is available at https://github.com/IVGSZ/Flash-VStream.
|
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding.
|
https://arxiv.org/abs/2506.23825v1
|
https://arxiv.org/pdf/2506.23825v1.pdf
| null |
[
"Haoji Zhang",
"Yiqin Wang",
"Yansong Tang",
"Yong liu",
"Jiashi Feng",
"Xiaojie Jin"
] |
[
"cross-modal alignment",
"EgoSchema",
"MME",
"MVBench",
"Video MME",
"Video Understanding"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ddl-a-dataset-for-interpretable-deepfake
|
2506.23292
| null | null |
DDL: A Dataset for Interpretable Deepfake Detection and Localization in Real-World Scenarios
|
Recent advances in AIGC have exacerbated the misuse of malicious deepfake content, making the development of reliable deepfake detection methods an essential means to address this challenge. Although existing deepfake detection models demonstrate outstanding performance in detection metrics, most methods only provide simple binary classification results, lacking interpretability. In critical domains such as law, interpretability is crucial for enhancing the credibility and authority of decisions. Recent studies attempt to improve the interpretability of classification results by providing spatial manipulation masks or temporal forgery segments. However, the practical effectiveness of these methods remains suboptimal due to limitations of the forgery data. Most current deepfake datasets predominantly offer binary labels, only a few datasets with localization annotations. However, they suffer from restricted forgery scenarios, limited diversity in deepfake types, and insufficient data scale, making them inadequate for complex real-world scenarios. To address this predicament, we construct a novel large-scale deepfake detection and localization ($\textbf{DDL}$) dataset containing over $\textbf{1.8M}$ forged samples and encompassing up to $\textbf{75}$ distinct deepfake methods. The DDL design incorporates four key innovations: (1) $\textbf{Diverse Forgery Scenarios}$, (2) $\textbf{Comprehensive Deepfake Methods}$, (3) $\textbf{Varied Manipulation Modes}$, and (4) $\textbf{Fine-grained Forgery Annotations}$. Through these improvements, our DDL not only provides a more challenging benchmark for complex real-world forgeries, but also offers crucial support for building next-generation deepfake detection, localization, and interpretability methods. The DDL dataset project page is on https://deepfake-workshop-ijcai2025.github.io/main/index.html.
| null |
https://arxiv.org/abs/2506.23292v1
|
https://arxiv.org/pdf/2506.23292v1.pdf
| null |
[
"Changtao Miao",
"Yi Zhang",
"Weize Gao",
"Man Luo",
"Weiwei Feng",
"Zhiya Tan",
"Jianshu Li",
"Ajian Liu",
"Yunfeng Diao",
"Qi Chu",
"Tao Gong",
"Zhe Li",
"Weibin Yao",
"Joey Tianyi Zhou"
] |
[
"Binary Classification",
"DeepFake Detection",
"Face Swapping"
] | 2025-06-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/beyond-spatial-frequency-pixel-wise-temporal
|
2507.02398
| null | null |
Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection
|
We introduce a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, which traditional spatial frequency-based detectors often overlook. Traditional detectors represent temporal information merely by stacking spatial frequency spectra across frames, resulting in the failure to detect temporal artifacts in the pixel plane. Our approach performs a 1D Fourier transform on the time axis for each pixel, extracting features highly sensitive to temporal inconsistencies, especially in areas prone to unnatural movements. To precisely locate regions containing the temporal artifacts, we introduce an attention proposal module trained in an end-to-end manner. Additionally, our joint transformer module effectively integrates pixel-wise temporal frequency features with spatio-temporal context features, expanding the range of detectable forgery artifacts. Our framework represents a significant advancement in deepfake video detection, providing robust performance across diverse and challenging detection scenarios.
| null |
https://arxiv.org/abs/2507.02398v2
|
https://arxiv.org/pdf/2507.02398v2.pdf
| null |
[
"TaeHoon Kim",
"Jongwook Choi",
"Yonghyun Jeong",
"Haeun Noh",
"Jaejun Yoo",
"Seungryul Baek",
"Jongwon Choi"
] |
[
"Face Swapping"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/corrdetail-visual-detail-enhanced-self
|
2507.05302
| null | null |
CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection
|
With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection.Existing techniques for face forgery detection can broadly be categorized into two primary groups: visual-based methods and multimodal approaches. The former often lacks clear explanations for forgery details, while the latter, which merges visual and linguistic modalities, is more prone to the issue of hallucinations.To address these shortcomings, we introduce a visual detail enhanced self-correction framework, designated CorrDetail, for interpretable face forgery detection. CorrDetail is meticulously designed to rectify authentic forgery details when provided with error-guided questioning, with the aim of fostering the ability to uncover forgery details rather than yielding hallucinated responses. Additionally, to bolster the reliability of its findings, a visual fine-grained detail enhancement module is incorporated, supplying CorrDetail with more precise visual forgery details. Ultimately, a fusion decision strategy is devised to further augment the model's discriminative capacity in handling extreme samples, through the integration of visual information compensation and model bias reduction.Experimental results demonstrate that CorrDetail not only achieves state-of-the-art performance compared to the latest methodologies but also excels in accurately identifying forged details, all while exhibiting robust generalization capabilities.
| null |
https://arxiv.org/abs/2507.05302v1
|
https://arxiv.org/pdf/2507.05302v1.pdf
| null |
[
"Binjia Zhou",
"Hengrui Lou",
"Lizhe Chen",
"Haoyuan Li",
"Dawei Luo",
"Shuai Chen",
"Jie Lei",
"Zunlei Feng",
"Yijun Bei"
] |
[
"Face Swapping",
"Image Generation"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spazer-spatial-semantic-progressive-reasoning
|
2506.21924
| null | null |
SPAZER: Spatial-Semantic Progressive Reasoning Agent for Zero-shot 3D Visual Grounding
|
3D Visual Grounding (3DVG) aims to localize target objects within a 3D scene based on natural language queries. To alleviate the reliance on costly 3D training data, recent studies have explored zero-shot 3DVG by leveraging the extensive knowledge and powerful reasoning capabilities of pre-trained LLMs and VLMs. However, existing paradigms tend to emphasize either spatial (3D-based) or semantic (2D-based) understanding, limiting their effectiveness in complex real-world applications. In this work, we introduce SPAZER - a VLM-driven agent that combines both modalities in a progressive reasoning framework. It first holistically analyzes the scene and produces a 3D rendering from the optimal viewpoint. Based on this, anchor-guided candidate screening is conducted to perform a coarse-level localization of potential objects. Furthermore, leveraging retrieved relevant 2D camera images, 3D-2D joint decision-making is efficiently performed to determine the best-matching object. By bridging spatial and semantic reasoning neural streams, SPAZER achieves robust zero-shot grounding without training on 3D-labeled data. Extensive experiments on ScanRefer and Nr3D benchmarks demonstrate that SPAZER significantly outperforms previous state-of-the-art zero-shot methods, achieving notable gains of 9.0% and 10.9% in accuracy.
| null |
https://arxiv.org/abs/2506.21924v1
|
https://arxiv.org/pdf/2506.21924v1.pdf
| null |
[
"Zhao Jin",
"Rong-Cheng Tu",
"Jingyi Liao",
"Wenhao Sun",
"Xiao Luo",
"Shunyu Liu",
"DaCheng Tao"
] |
[
"3D visual grounding",
"Natural Language Queries",
"Visual Grounding"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/atstrack-enhancing-visual-language-tracking
|
2507.00454
| null | null |
ATSTrack: Enhancing Visual-Language Tracking by Aligning Temporal and Spatial Scales
|
A main challenge of Visual-Language Tracking (VLT) is the misalignment between visual inputs and language descriptions caused by target movement. Previous trackers have explored many effective feature modification methods to preserve more aligned features. However, an important yet unexplored factor ultimately hinders their capability, which is the inherent differences in the temporal and spatial scale of information between visual and language inputs. To address this issue, we propose a novel visual-language tracker that enhances the effect of feature modification by \textbf{A}ligning \textbf{T}emporal and \textbf{S}patial scale of different input components, named as \textbf{ATSTrack}. Specifically, we decompose each language description into phrases with different attributes based on their temporal and spatial correspondence with visual inputs, and modify their features in a fine-grained manner. Moreover, we introduce a Visual-Language token that comprises modified linguistic information from the previous frame to guide the model to extract visual features that are more relevant to language description, thereby reducing the impact caused by the differences in spatial scale. Experimental results show that our proposed ATSTrack achieves performance comparable to existing methods. Our code will be released.
| null |
https://arxiv.org/abs/2507.00454v1
|
https://arxiv.org/pdf/2507.00454v1.pdf
| null |
[
"Yihao Zhen",
"Qiang Wang",
"Yu Qiao",
"Liangqiong Qu",
"Huijie Fan"
] |
[] | 2025-07-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/r1-track-direct-application-of-mllms-to
|
2506.21980
| null | null |
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
|
Visual single object tracking aims to continuously localize and estimate the scale of a target in subsequent video frames, given only its initial state in the first frame. This task has traditionally been framed as a template matching problem, evolving through major phases including correlation filters, two-stream networks, and one-stream networks with significant progress achieved. However, these methods typically require explicit classification and regression modeling, depend on supervised training with large-scale datasets, and are limited to the single task of tracking, lacking flexibility. In recent years, multi-modal large language models (MLLMs) have advanced rapidly. Open-source models like Qwen2.5-VL, a flagship MLLMs with strong foundational capabilities, demonstrate excellent performance in grounding tasks. This has spurred interest in applying such models directly to visual tracking. However, experiments reveal that Qwen2.5-VL struggles with template matching between image pairs (i.e., tracking tasks). Inspired by deepseek-R1, we fine-tuned Qwen2.5-VL using the group relative policy optimization (GRPO) reinforcement learning method on a small-scale dataset with a rule-based reward function. The resulting model, R1-Track, achieved notable performance on the GOT-10k benchmark. R1-Track supports flexible initialization via bounding boxes or text descriptions while retaining most of the original model's general capabilities. And we further discuss potential improvements for R1-Track. This rough technical report summarizes our findings as of May 2025.
|
Visual single object tracking aims to continuously localize and estimate the scale of a target in subsequent video frames, given only its initial state in the first frame.
|
https://arxiv.org/abs/2506.21980v2
|
https://arxiv.org/pdf/2506.21980v2.pdf
| null |
[
"Biao Wang",
"Wenwen Li"
] |
[
"Object Tracking",
"Template Matching",
"Visual Object Tracking",
"Visual Tracking"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mamba-fetrack-v2-revisiting-state-space-model
|
2506.23783
| null | null |
Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
|
Combining traditional RGB cameras with bio-inspired event cameras for robust object tracking has garnered increasing attention in recent years. However, most existing multimodal tracking algorithms depend heavily on high-complexity Vision Transformer architectures for feature extraction and fusion across modalities. This not only leads to substantial computational overhead but also limits the effectiveness of cross-modal interactions. In this paper, we propose an efficient RGB-Event object tracking framework based on the linear-complexity Vision Mamba network, termed Mamba-FETrack V2. Specifically, we first design a lightweight Prompt Generator that utilizes embedded features from each modality, together with a shared prompt pool, to dynamically generate modality-specific learnable prompt vectors. These prompts, along with the modality-specific embedded features, are then fed into a Vision Mamba-based FEMamba backbone, which facilitates prompt-guided feature extraction, cross-modal interaction, and fusion in a unified manner. Finally, the fused representations are passed to the tracking head for accurate target localization. Extensive experimental evaluations on multiple RGB-Event tracking benchmarks, including short-term COESOT dataset and long-term datasets, i.e., FE108 and FELT V2, demonstrate the superior performance and efficiency of the proposed tracking framework. The source code and pre-trained models will be released on https://github.com/Event-AHU/Mamba_FETrack
|
These prompts, along with the modality-specific embedded features, are then fed into a Vision Mamba-based FEMamba backbone, which facilitates prompt-guided feature extraction, cross-modal interaction, and fusion in a unified manner.
|
https://arxiv.org/abs/2506.23783v1
|
https://arxiv.org/pdf/2506.23783v1.pdf
| null |
[
"Shiao Wang",
"Ju Huang",
"Qingchuan Ma",
"Jinfeng Gao",
"Chunyi Xu",
"Xiao Wang",
"Lan Chen",
"Bo Jiang"
] |
[
"Mamba",
"Object Tracking",
"Visual Object Tracking"
] | 2025-06-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/google-research/vision_transformer",
"description": "The **Vision Transformer**, or **ViT**, is a model for image classification that employs a [Transformer](https://paperswithcode.com/method/transformer)-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard [Transformer](https://paperswithcode.com/method/transformer) encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.",
"full_name": "Vision Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Vision Transformer",
"source_title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
"source_url": "https://arxiv.org/abs/2010.11929v2"
},
{
"code_snippet_url": "https://github.com/state-spaces/mamba",
"description": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pre-training and downstream evaluation.",
"full_name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"introduced_year": 2000,
"main_collection": null,
"name": "Mamba",
"source_title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"source_url": "https://arxiv.org/abs/2312.00752v2"
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/what-you-have-is-what-you-track-adaptive-and
|
2507.05899
| null | null |
What You Have is What You Track: Adaptive and Robust Multimodal Tracking
|
Multimodal data is known to be helpful for visual tracking by improving robustness to appearance variations. However, sensor synchronization challenges often compromise data availability, particularly in video settings where shortages can be temporal. Despite its importance, this area remains underexplored. In this paper, we present the first comprehensive study on tracker performance with temporally incomplete multimodal data. Unsurprisingly, under such a circumstance, existing trackers exhibit significant performance degradation, as their rigid architectures lack the adaptability needed to effectively handle missing modalities. To address these limitations, we propose a flexible framework for robust multimodal tracking. We venture that a tracker should dynamically activate computational units based on missing data rates. This is achieved through a novel Heterogeneous Mixture-of-Experts fusion mechanism with adaptive complexity, coupled with a video-level masking strategy that ensures both temporal consistency and spatial completeness which is critical for effective video tracking. Surprisingly, our model not only adapts to varying missing rates but also adjusts to scene complexity. Extensive experiments show that our model achieves SOTA performance across 9 benchmarks, excelling in both conventional complete and missing modality settings. The code and benchmark will be publicly available at https://github.com/supertyd/FlexTrack/tree/main.
| null |
https://arxiv.org/abs/2507.05899v1
|
https://arxiv.org/pdf/2507.05899v1.pdf
| null |
[
"Yuedong Tan",
"Jiawei Shao",
"Eduard Zamfir",
"Ruanjun Li",
"Zhaochong An",
"Chao Ma",
"Danda Paudel",
"Luc van Gool",
"Radu Timofte",
"Zongwei Wu"
] |
[
"Mixture-of-Experts",
"Visual Tracking"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/visual-and-memory-dual-adapter-for-multi
|
2506.23972
| null | null |
Visual and Memory Dual Adapter for Multi-Modal Object Tracking
|
Prompt-learning-based multi-modal trackers have achieved promising progress by employing lightweight visual adapters to incorporate auxiliary modality features into frozen foundation models. However, existing approaches often struggle to learn reliable prompts due to limited exploitation of critical cues across frequency and temporal domains. In this paper, we propose a novel visual and memory dual adapter (VMDA) to construct more robust and discriminative representations for multi-modal tracking. Specifically, we develop a simple but effective visual adapter that adaptively transfers discriminative cues from auxiliary modality to dominant modality by jointly modeling the frequency, spatial, and channel-wise features. Additionally, we design the memory adapter inspired by the human memory mechanism, which stores global temporal cues and performs dynamic update and retrieval operations to ensure the consistent propagation of reliable temporal information across video sequences. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the various multi-modal tracking tasks, including RGB-Thermal, RGB-Depth, and RGB-Event tracking. Code and models are available at https://github.com/xuboyue1999/mmtrack.git.
| null |
https://arxiv.org/abs/2506.23972v1
|
https://arxiv.org/pdf/2506.23972v1.pdf
| null |
[
"Boyue Xu",
"Ruichao Hou",
"Tongwei Ren",
"Gangshan Wu"
] |
[
"Object Tracking",
"Prompt Learning"
] | 2025-06-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Adapter",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Adapter",
"source_title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing",
"source_url": "https://arxiv.org/abs/2101.03289v5"
}
] |
https://paperswithcode.com/paper/fmocc-tpv-driven-flow-matching-for-3d
|
2507.02250
| null | null |
FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model
|
3D semantic occupancy prediction plays a pivotal role in autonomous driving. However, inherent limitations of fewframe images and redundancy in 3D space compromise prediction accuracy for occluded and distant scenes. Existing methods enhance performance by fusing historical frame data, which need additional data and significant computational resources. To address these issues, this paper propose FMOcc, a Tri-perspective View (TPV) refinement occupancy network with flow matching selective state space model for few-frame 3D occupancy prediction. Firstly, to generate missing features, we designed a feature refinement module based on a flow matching model, which is called Flow Matching SSM module (FMSSM). Furthermore, by designing the TPV SSM layer and Plane Selective SSM (PS3M), we selectively filter TPV features to reduce the impact of air voxels on non-air voxels, thereby enhancing the overall efficiency of the model and prediction capability for distant scenes. Finally, we design the Mask Training (MT) method to enhance the robustness of FMOcc and address the issue of sensor data loss. Experimental results on the Occ3D-nuScenes and OpenOcc datasets show that our FMOcc outperforms existing state-of-theart methods. Our FMOcc with two frame input achieves notable scores of 43.1% RayIoU and 39.8% mIoU on Occ3D-nuScenes validation, 42.6% RayIoU on OpenOcc with 5.4 G inference memory and 330ms inference time.
| null |
https://arxiv.org/abs/2507.02250v1
|
https://arxiv.org/pdf/2507.02250v1.pdf
| null |
[
"Jiangxia Chen",
"Tongyuan Huang",
"Ke Song"
] |
[
"3D Semantic Occupancy Prediction",
"Autonomous Driving",
"Prediction"
] | 2025-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/graphgsocc-semantic-geometric-graph
|
2506.14825
| null | null |
GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction
|
Addressing the task of 3D semantic occupancy prediction for autonomous driving, we tackle two key issues in existing 3D Gaussian Splatting (3DGS) methods: (1) unified feature aggregation neglecting semantic correlations among similar categories and across regions, (2) boundary ambiguities caused by the lack of geometric constraints in MLP iterative optimization and (3) biased issues in dynamic-static object coupling optimization. We propose the GraphGSOcc model, a novel framework that combines semantic and geometric graph Transformer and decouples dynamic-static objects optimization for 3D Gaussian Splatting-based Occupancy Prediction. We propose the Dual Gaussians Graph Attenntion, which dynamically constructs dual graph structures: a geometric graph adaptively calculating KNN search radii based on Gaussian poses, enabling large-scale Gaussians to aggregate features from broader neighborhoods while compact Gaussians focus on local geometric consistency; a semantic graph retaining top-M highly correlated nodes via cosine similarity to explicitly encode semantic relationships within and across instances. Coupled with the Multi-scale Graph Attention framework, fine-grained attention at lower layers optimizes boundary details, while coarsegrained attention at higher layers models object-level topology. On the other hand, we decouple dynamic and static objects by leveraging semantic probability distributions and design a Dynamic-Static Decoupled Gaussian Attention mechanism to optimize the prediction performance for both dynamic objects and static scenes. GraphGSOcc achieves state-ofthe-art performance on the SurroundOcc-nuScenes, Occ3D-nuScenes, OpenOcc and KITTI occupancy benchmarks. Experiments on the SurroundOcc dataset achieve an mIoU of 25.20%, reducing GPU memory to 6.8 GB, demonstrating a 1.97% mIoU improvement and 13.7% memory reduction compared to GaussianWorld.
| null |
https://arxiv.org/abs/2506.14825v2
|
https://arxiv.org/pdf/2506.14825v2.pdf
| null |
[
"Ke Song",
"Yunhe Wu",
"Chunchit Siu",
"Huiyuan Xiong"
] |
[
"3DGS",
"3D Semantic Occupancy Prediction",
"Autonomous Driving",
"GPU",
"Graph Attention"
] | 2025-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/youtube-occ-learning-indoor-3d-semantic
|
2506.18266
| null | null |
YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos
|
3D semantic occupancy prediction in the past was considered to require precise geometric relationships in order to enable effective training. However, in complex indoor environments, the large-scale and widespread collection of data, along with the necessity for fine-grained annotations, becomes impractical due to the complexity of data acquisition setups and privacy concerns. In this paper, we demonstrate that 3D spatially-accurate training can be achieved using only indoor Internet data, without the need for any pre-knowledge of intrinsic or extrinsic camera parameters. In our framework, we collect a web dataset, YouTube-Occ, which comprises house tour videos from YouTube, providing abundant real house scenes for 3D representation learning. Upon on this web dataset, we establish a fully self-supervised model to leverage accessible 2D prior knowledge for reaching powerful 3D indoor perception. Specifically, we harness the advantages of the prosperous vision foundation models, distilling the 2D region-level knowledge into the occupancy network by grouping the similar pixels into superpixels. Experimental results show that our method achieves state-of-the-art zero-shot performance on two popular benchmarks (NYUv2 and OccScanNet
| null |
https://arxiv.org/abs/2506.18266v1
|
https://arxiv.org/pdf/2506.18266v1.pdf
| null |
[
"Haoming Chen",
"Lichen Yuan",
"Tianfang Sun",
"Jingyu Gong",
"Xin Tan",
"Zhizhong Zhang",
"Yuan Xie"
] |
[
"3D Semantic Occupancy Prediction",
"Representation Learning",
"Superpixels"
] | 2025-06-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/large-language-models-for-combinatorial-1
|
2507.03637
| null | null |
Large Language Models for Combinatorial Optimization: A Systematic Review
|
This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conduct a literature search via Scopus and Google Scholar, examining over 2,000 publications. We assess publications against four inclusion and four exclusion criteria related to their language, research focus, publication year, and type. Eventually, we select 103 studies. We classify these studies into semantic categories and topics to provide a comprehensive overview of the field, including the tasks performed by LLMs, the architectures of LLMs, the existing datasets specifically designed for evaluating LLMs in CO, and the field of application. Finally, we identify future directions for leveraging LLMs in this field.
| null |
https://arxiv.org/abs/2507.03637v1
|
https://arxiv.org/pdf/2507.03637v1.pdf
| null |
[
"Francesca Da Ros",
"Michael Soprano",
"Luca Di Gaspero",
"Kevin Roitero"
] |
[
"Combinatorial Optimization"
] | 2025-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lscd-lomb-scargle-conditioned-diffusion-for
|
2506.17039
| null | null |
LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
|
Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Transform (FFT) which assumes uniform sampling, therefore requiring prior interpolation that can distort the spectra. To address this limitation, we introduce a differentiable Lomb--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data. We integrate this layer into a novel score-based diffusion model (LSCD) for time series imputation conditioned on the entire signal spectrum. Experiments on synthetic and real-world benchmarks demonstrate that our method recovers missing data more accurately than purely time-domain baselines, while simultaneously producing consistent frequency estimates. Crucially, our method can be easily integrated into learning frameworks, enabling broader adoption of spectral guidance in machine learning approaches involving incomplete or irregular data.
| null |
https://arxiv.org/abs/2506.17039v1
|
https://arxiv.org/pdf/2506.17039v1.pdf
| null |
[
"Elizabeth Fons",
"Alejandro Sztrajman",
"Yousef El-Laham",
"Luciana Ferrer",
"Svitlana Vyetrenko",
"Manuela Veloso"
] |
[
"Imputation",
"Time Series"
] | 2025-06-20T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/double-diffusion-diffusion-conditioned
|
2506.23053
| null | null |
Double-Diffusion: Diffusion Conditioned Diffusion Probabilistic Model For Air Quality Prediction
|
Air quality prediction is a challenging forecasting task due to its spatio-temporal complexity and the inherent dynamics as well as uncertainty. Most of the current models handle these two challenges by applying Graph Neural Networks or known physics principles, and quantifying stochasticity through probabilistic networks like Diffusion models. Nevertheless, finding the right balancing point between the certainties and uncertainties remains an open question. Therefore, we propose Double-Diffusion, a novel diffusion probabilistic model that harnesses the power of known physics to guide air quality forecasting with stochasticity. To the best of our knowledge, while precedents have been made of using conditional diffusion models to predict air pollution, this is the first attempt to use physics as a conditional generative approach for air quality prediction. Along with a sampling strategy adopted from image restoration and a new denoiser architecture, Double-Diffusion ranks first in most evaluation scenarios across two real-life datasets compared with other probabilistic models, it also cuts inference time by 50% to 30% while enjoying an increase between 3-12% in Continuous Ranked Probabilistic Score (CRPS).
| null |
https://arxiv.org/abs/2506.23053v1
|
https://arxiv.org/pdf/2506.23053v1.pdf
| null |
[
"Hanlin Dong",
"Arian Prabowo",
"Hao Xue",
"Flora D. Salim"
] |
[
"Image Restoration"
] | 2025-06-29T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/memba-membrane-driven-parameter-efficient
|
2506.18184
| null | null |
Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba
|
State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers, with Mamba demonstrating impressive efficiency and scalability. As these models grow increasingly larger, the need for Parameter-Efficient Fine-Tuning (PEFT) methods becomes critical to adapt pre-trained Mamba to downstream tasks without prohibitive computational costs. However, previous approaches simply apply traditional Transformer-tailored PEFT methods without addressing the unique temporal processing dynamics of SSMs. To address this limitation, we propose Memba, a membrane-driven PEFT approach specifically designed for Mamba. Memba introduces Leaky Integrate Membrane (LIM) neurons as bio-inspired gating mechanisms that naturally accumulate membrane potentials over time, enhancing selective information retention. By strategically combining LIM neurons with Low-Rank Adaptations (LoRA) and cross-layer membrane transfer, our approach significantly improves Mamba's temporal modeling capabilities. Extensive experiments across language and vision tasks demonstrate that Memba achieves substantial improvements over existing PEFT methods. The code is available at https://github.com/Intelligent-Computing-Lab-Yale/Memba.
| null |
https://arxiv.org/abs/2506.18184v1
|
https://arxiv.org/pdf/2506.18184v1.pdf
| null |
[
"DongHyun Lee",
"Yuhang Li",
"Ruokai Yin",
"Shiting Xiao",
"Priyadarshini Panda"
] |
[
"Mamba",
"parameter-efficient fine-tuning",
"State Space Models"
] | 2025-06-22T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/state-spaces/mamba",
"description": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pre-training and downstream evaluation.",
"full_name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"introduced_year": 2000,
"main_collection": null,
"name": "Mamba",
"source_title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"source_url": "https://arxiv.org/abs/2312.00752v2"
}
] |
https://paperswithcode.com/paper/tvg-slam-robust-gaussian-splatting-slam-with
|
2506.23207
| null | null |
TVG-SLAM: Robust Gaussian Splatting SLAM with Tri-view Geometric Constraints
|
Recent advances in 3D Gaussian Splatting (3DGS) have enabled RGB-only SLAM systems to achieve high-fidelity scene representation. However, the heavy reliance of existing systems on photometric rendering loss for camera tracking undermines their robustness, especially in unbounded outdoor environments with severe viewpoint and illumination changes. To address these challenges, we propose TVG-SLAM, a robust RGB-only 3DGS SLAM system that leverages a novel tri-view geometry paradigm to ensure consistent tracking and high-quality mapping. We introduce a dense tri-view matching module that aggregates reliable pairwise correspondences into consistent tri-view matches, forming robust geometric constraints across frames. For tracking, we propose Hybrid Geometric Constraints, which leverage tri-view matches to construct complementary geometric cues alongside photometric loss, ensuring accurate and stable pose estimation even under drastic viewpoint shifts and lighting variations. For mapping, we propose a new probabilistic initialization strategy that encodes geometric uncertainty from tri-view correspondences into newly initialized Gaussians. Additionally, we design a Dynamic Attenuation of Rendering Trust mechanism to mitigate tracking drift caused by mapping latency. Experiments on multiple public outdoor datasets show that our TVG-SLAM outperforms prior RGB-only 3DGS-based SLAM systems. Notably, in the most challenging dataset, our method improves tracking robustness, reducing the average Absolute Trajectory Error (ATE) by 69.0\% while achieving state-of-the-art rendering quality. The implementation of our method will be released as open-source.
| null |
https://arxiv.org/abs/2506.23207v1
|
https://arxiv.org/pdf/2506.23207v1.pdf
| null |
[
"Zhen Tan",
"Xieyuanli Chen",
"Lei Feng",
"Yangbing Ge",
"Shuaifeng Zhi",
"Jiaxiong Liu",
"Dewen Hu"
] |
[
"3DGS",
"Pose Estimation"
] | 2025-06-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/outdoor-monocular-slam-with-global-scale
|
2507.03737
| null | null |
Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps
|
3D Gaussian Splatting (3DGS) has become a popular solution in SLAM due to its high-fidelity and real-time novel view synthesis performance. However, some previous 3DGS SLAM methods employ a differentiable rendering pipeline for tracking, \textbf{lack geometric priors} in outdoor scenes. Other approaches introduce separate tracking modules, but they accumulate errors with significant camera movement, leading to \textbf{scale drift}. To address these challenges, we propose a robust RGB-only outdoor 3DGS SLAM method: S3PO-GS. Technically, we establish a self-consistent tracking module anchored in the 3DGS pointmap, which avoids cumulative scale drift and achieves more precise and robust tracking with fewer iterations. Additionally, we design a patch-based pointmap dynamic mapping module, which introduces geometric priors while avoiding scale ambiguity. This significantly enhances tracking accuracy and the quality of scene reconstruction, making it particularly suitable for complex outdoor environments. Our experiments on the Waymo, KITTI, and DL3DV datasets demonstrate that S3PO-GS achieves state-of-the-art results in novel view synthesis and outperforms other 3DGS SLAM methods in tracking accuracy. Project page: https://3dagentworld.github.io/S3PO-GS/.
| null |
https://arxiv.org/abs/2507.03737v1
|
https://arxiv.org/pdf/2507.03737v1.pdf
| null |
[
"Chong Cheng",
"Sicheng Yu",
"Zijian Wang",
"Yifan Zhou",
"Hao Wang"
] |
[
"3DGS",
"Novel View Synthesis"
] | 2025-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sara-selective-and-adaptive-retrieval
|
2507.05633
| null | null |
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
|
Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in retrieved documents. Pure compression-based approaches reduce input size but often discard fine-grained details essential for factual accuracy. We propose SARA, a unified RAG framework that balances local precision and global knowledge coverage under tight context budgets. SARA combines natural-language text snippets with semantic compression vectors to jointly enhance context efficiency and answer correctness. It represents contexts at two complementary levels: 1) fine-grained natural-language spans that preserve critical entities and numerical values, and 2) compact, interpretable vectors that summarize high-level semantics. An iterative evidence-selection module employs the compression vectors for dynamic reranking of contexts. Across 9 datasets and 5 open-source LLMs spanning 3 model families (Mistral, Llama, and Gemma), SARA consistently improves answer relevance (+17.71), answer correctness (+13.72), and semantic similarity (+15.53), demonstrating the importance of integrating textual and compressed representations for robust, context-efficient RAG.
| null |
https://arxiv.org/abs/2507.05633v1
|
https://arxiv.org/pdf/2507.05633v1.pdf
| null |
[
"Yiqiao Jin",
"Kartik Sharma",
"Vineeth Rakesh",
"Yingtong Dou",
"Menghai Pan",
"Mahashweta Das",
"Srijan Kumar"
] |
[
"Evidence Selection",
"RAG",
"Reranking",
"Retrieval",
"Retrieval-augmented Generation",
"Semantic Compression",
"Semantic Similarity",
"Semantic Textual Similarity"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.",
"full_name": "Linear Warmup With Linear Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Linear Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!\r\n\r\n\r\n“How do I get a full refund from Expedia?\r\nHow do I get a full refund from Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!",
"full_name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Refunds@Expedia|||How do I get a full refund from Expedia?",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v5"
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
},
{
"code_snippet_url": null,
"description": "**BART** is a [denoising autoencoder](https://paperswithcode.com/method/denoising-autoencoder) for pretraining sequence-to-sequence models. It is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard [Transformer](https://paperswithcode.com/method/transformer)-based neural machine translation architecture. It uses a standard seq2seq/NMT architecture with a bidirectional encoder (like [BERT](https://paperswithcode.com/method/bert)) and a left-to-right decoder (like [GPT](https://paperswithcode.com/method/gpt)). This means the encoder's attention mask is fully visible, like BERT, and the decoder's attention mask is causal, like [GPT2](https://paperswithcode.com/method/gpt-2).",
"full_name": "BART",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "BART",
"source_title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
"source_url": "https://arxiv.org/abs/1910.13461v1"
},
{
"code_snippet_url": "",
"description": "**Retriever-Augmented Generation**, or **RAG**, is a type of language generation model that combines pre-trained parametric and non-parametric memory for language generation. Specifically, the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. For query $x$, Maximum Inner Product Search (MIPS) is used to find the top-K documents $z\\_{i}$. For final prediction $y$, we treat $z$ as a latent variable and marginalize over seq2seq predictions given different documents.",
"full_name": "RAG",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "RAG",
"source_title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks",
"source_url": "https://arxiv.org/abs/2005.11401v4"
}
] |
https://paperswithcode.com/paper/orthorank-token-selection-via-sink-token
|
2507.03865
| null | null |
OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference
|
Attention mechanisms are central to the success of large language models (LLMs), enabling them to capture intricate token dependencies and implicitly assign importance to each token. Recent studies have revealed the sink token, which receives disproportionately high attention despite their limited semantic role. In this paper, we first expand the relationship between the sink token and other tokens, moving beyond attention to explore their similarity in hidden states, considering the layer depth. We observe that as the layers get deeper, the cosine similarity between the normalized hidden states of the sink token and those of other tokens increases, and that the normalized hidden states of the sink token exhibit negligible changes. These imply that other tokens consistently are directed toward the sink token throughout the layers. Next, we propose a dynamic token selection method, called OrthoRank, using these findings to select important tokens. Specifically, in a certain layer, we define token importance by the speed at which the token moves toward the sink token. This is converted into orthogonality with the sink token, meaning that tokens that are more orthogonal to the sink token are assigned greater importance. Finally, through extensive experiments, we demonstrated that our method results in lower perplexity and higher zero-shot accuracy compared to layer pruning methods at the same sparsity ratio with comparable throughput, while also achieving superior performance on LongBench.
| null |
https://arxiv.org/abs/2507.03865v1
|
https://arxiv.org/pdf/2507.03865v1.pdf
| null |
[
"Seungjun Shin",
"Jaehoon Oh",
"Dokwan Oh"
] |
[] | 2025-07-05T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
},
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/q-frame-query-aware-frame-selection-and-multi
|
2506.22139
| null | null |
Q-Frame: Query-aware Frame Selection and Multi-Resolution Adaptation for Video-LLMs
|
Multimodal Large Language Models (MLLMs) have demonstrated significant success in visual understanding tasks. However, challenges persist in adapting these models for video comprehension due to the large volume of data and temporal complexity. Existing Video-LLMs using uniform frame sampling often struggle to capture the query-related crucial spatiotemporal clues of videos effectively. In this paper, we introduce Q-Frame, a novel approach for adaptive frame selection and multi-resolution scaling tailored to the video's content and the specific query. Q-Frame employs a training-free, plug-and-play strategy generated by a text-image matching network like CLIP, utilizing the Gumbel-Max trick for efficient frame selection. Q-Frame allows Video-LLMs to process more frames without exceeding computational limits, thereby preserving critical temporal and spatial information. We demonstrate Q-Frame's effectiveness through extensive experiments on benchmark datasets, including MLVU, LongVideoBench, and Video-MME, illustrating its superiority over existing methods and its applicability across various video understanding tasks.
| null |
https://arxiv.org/abs/2506.22139v2
|
https://arxiv.org/pdf/2506.22139v2.pdf
| null |
[
"Shaojie Zhang",
"Jiahui Yang",
"Jianqin Yin",
"Zhenbo Luo",
"Jian Luan"
] |
[
"MME",
"Video MME",
"Video Understanding"
] | 2025-06-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
}
] |
https://paperswithcode.com/paper/loom-scope-a-comprehensive-and-efficient-long
|
2507.04723
| null | null |
LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation framework
|
Long-context processing has become a fundamental capability for large language models~(LLMs). To assess model's long-context performance, numerous long-context evaluation benchmarks have been proposed. However, variations in evaluation settings across these benchmarks lead to inconsistent results, making it difficult to draw reliable comparisons. Besides, the high computational cost of long-context evaluation poses a significant barrier for the community to conduct comprehensive assessments of long-context models. In this paper, we propose LOOM-Scope, a comprehensive and efficient framework for long-context evaluation. LOOM-Scope standardizes evaluation settings across diverse benchmarks, supports deployment of efficient long-context inference acceleration methods, and introduces a holistic yet lightweight benchmark suite to evaluate models comprehensively. Homepage: https://loomscope.github.io
|
Long-context processing has become a fundamental capability for large language models~(LLMs).
|
https://arxiv.org/abs/2507.04723v1
|
https://arxiv.org/pdf/2507.04723v1.pdf
| null |
[
"Zecheng Tang",
"Haitian Wang",
"Quantong Qiu",
"Baibei Ji",
"Ruoxi Sun",
"Keyan Zhou",
"Juntao Li",
"Min Zhang"
] |
[] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/llmthinkbench-towards-basic-math-reasoning
|
2507.04023
| null | null |
LLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models
|
Large Language Models (LLMs) have achieved remarkable performance on complex mathematical benchmarks, yet often struggle with simple arithmetic tasks and exhibit a tendency toward over-explaining or "overthinking" answers. To systematically assess this phenomenon, we introduce LLMThinkBench, a modular benchmarking framework that enables researchers to evaluate basic math reasoning and overthinking in LLMs. The framework provides 14 configurable math tasks with randomized test data generation and robust parsing strategies. Researchers can quantify overthinking using our Overthinking Score metric, which captures accuracy-verbosity tradeoffs through harmonic mean formulation. The tool offers flexible evaluation with a scalable vLLM/Transformers backend, multi-GPU support, and full configurability. Users can extend the tool with custom tasks, reproduce experiments with seeding, and generate detailed efficiency reports. Distributed as a pip-installable package with CLI and API access, LLMThinkBench provides researchers and practitioners an accessible, cost-effective alternative to expensive LLM-as-a-judge methods for diagnosing basic reasoning capabilities and efficiency analysis. Package can be installed as: pip install llmthinkbench
|
Distributed as a pip-installable package with CLI and API access, LLMThinkBench provides researchers and practitioners an accessible, cost-effective alternative to expensive LLM-as-a-judge methods for diagnosing basic reasoning capabilities and efficiency analysis.
|
https://arxiv.org/abs/2507.04023v1
|
https://arxiv.org/pdf/2507.04023v1.pdf
| null |
[
"Gaurav Srivastava",
"Aafiya Hussain",
"Sriram Srinivasan",
"Xuan Wang"
] |
[
"Benchmarking",
"GPU",
"Math"
] | 2025-07-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/memagent-reshaping-long-context-llm-with
|
2507.02259
| null | null |
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
|
Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing. We directly optimize for long-text tasks in an end-to-end fashion and introduce a novel agent workflow, MemAgent, which reads text in segments and updates the memory using an overwrite strategy. We extend the DAPO algorithm to facilitate training via independent-context multi-conversation generation. MemAgent has demonstrated superb long-context capabilities, being able to extrapolate from an 8K context trained on 32K text to a 3.5M QA task with performance loss < 5% and achieves 95%+ in 512K RULER test.
| null |
https://arxiv.org/abs/2507.02259v1
|
https://arxiv.org/pdf/2507.02259v1.pdf
| null |
[
"Hongli Yu",
"Tinghong Chen",
"Jiangtao Feng",
"Jiangjie Chen",
"Weinan Dai",
"Qiying Yu",
"Ya-Qin Zhang",
"Wei-Ying Ma",
"Jingjing Liu",
"Mingxuan Wang",
"Hao Zhou"
] |
[
"8k"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Dialogue-Adaptive Pre-training Objective (DAPO)** is a pre-training objective for dialogue adaptation, which is designed to measure qualities of dialogues from multiple important aspects, like Readability, Consistency and Fluency which have already been focused on by general LM pre-training objectives, and those also significant for assessing dialogues but ignored by general LM pre-training objectives, like Diversity and Specificity.",
"full_name": "Dialogue-Adaptive Pre-training Objective",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Dialog Adaptation",
"parent": null
},
"name": "DAPO",
"source_title": "Dialogue-adaptive Language Model Pre-training From Quality Estimation",
"source_url": "https://arxiv.org/abs/2009.04984v2"
}
] |
https://paperswithcode.com/paper/glm-4-1v-thinking-towards-versatile
|
2507.01006
| null | null |
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
|
We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. We then propose Reinforcement Learning with Curriculum Sampling (RLCS) to unlock the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document understanding. We open-source GLM-4.1V-9B-Thinking, which achieves state-of-the-art performance among models of comparable size. In a comprehensive evaluation across 28 public benchmarks, our model outperforms Qwen2.5-VL-7B on nearly all tasks and achieves comparable or even superior performance on 18 benchmarks relative to the significantly larger Qwen2.5-VL-72B. Notably, GLM-4.1V-9B-Thinking also demonstrates competitive or superior performance compared to closed-source models such as GPT-4o on challenging tasks including long document understanding and STEM reasoning, further underscoring its strong capabilities. Code, models and more information are released at https://github.com/THUDM/GLM-4.1V-Thinking.
|
In a comprehensive evaluation across 28 public benchmarks, our model outperforms Qwen2. 5-VL-7B on nearly all tasks and achieves comparable or even superior performance on 18 benchmarks relative to the significantly larger Qwen2. 5-VL-72B.
|
https://arxiv.org/abs/2507.01006v2
|
https://arxiv.org/pdf/2507.01006v2.pdf
| null |
[
"GLM-V Team",
":",
"Wenyi Hong",
"Wenmeng Yu",
"Xiaotao Gu",
"Guo Wang",
"Guobing Gan",
"Haomiao Tang",
"Jiale Cheng",
"Ji Qi",
"Junhui Ji",
"Lihang Pan",
"Shuaiqi Duan",
"Weihan Wang",
"Yan Wang",
"Yean Cheng",
"Zehai He",
"Zhe Su",
"Zhen Yang",
"Ziyang Pan",
"Aohan Zeng",
"Baoxu Wang",
"Boyan Shi",
"Changyu Pang",
"Chenhui Zhang",
"Da Yin",
"Fan Yang",
"Guoqing Chen",
"Jiazheng Xu",
"Jiali Chen",
"Jing Chen",
"Jinhao Chen",
"Jinghao Lin",
"Jinjiang Wang",
"Junjie Chen",
"Leqi Lei",
"Letian Gong",
"Leyi Pan",
"Mingzhi Zhang",
"Qinkai Zheng",
"Sheng Yang",
"Shi Zhong",
"Shiyu Huang",
"Shuyuan Zhao",
"Siyan Xue",
"Shangqin Tu",
"Shengbiao Meng",
"Tianshu Zhang",
"Tianwei Luo",
"Tianxiang Hao",
"Wenkai Li",
"Wei Jia",
"Xin Lyu",
"Xuancheng Huang",
"Yanling Wang",
"Yadong Xue",
"Yanfeng Wang",
"Yifan An",
"Yifan Du",
"Yiming Shi",
"Yiheng Huang",
"Yilin Niu",
"YuAn Wang",
"Yuanchang Yue",
"Yuchen Li",
"Yutao Zhang",
"Yuxuan Zhang",
"Zhanxiao Du",
"Zhenyu Hou",
"Zhao Xue",
"Zhengxiao Du",
"Zihan Wang",
"Peng Zhang",
"Debing Liu",
"Bin Xu",
"Juanzi Li",
"Minlie Huang",
"Yuxiao Dong",
"Jie Tang"
] |
[
"document understanding",
"Multimodal Reasoning",
"Video Understanding"
] | 2025-07-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/evaluating-language-models-for-threat
|
2507.02390
| null | null |
Evaluating Language Models For Threat Detection in IoT Security Logs
|
Log analysis is a relevant research field in cybersecurity as they can provide a source of information for the detection of threats to networks and systems. This paper presents a pipeline to use fine-tuned Large Language Models (LLMs) for anomaly detection and mitigation recommendation using IoT security logs. Utilizing classical machine learning classifiers as a baseline, three open-source LLMs are compared for binary and multiclass anomaly detection, with three strategies: zero-shot, few-shot prompting and fine-tuning using an IoT dataset. LLMs give better results on multi-class attack classification than the corresponding baseline models. By mapping detected threats to MITRE CAPEC, defining a set of IoT-specific mitigation actions, and fine-tuning the models with those actions, the models are able to provide a combined detection and recommendation guidance.
|
Log analysis is a relevant research field in cybersecurity as they can provide a source of information for the detection of threats to networks and systems.
|
https://arxiv.org/abs/2507.02390v1
|
https://arxiv.org/pdf/2507.02390v1.pdf
| null |
[
"Jorge J. Tejero-Fernández",
"Alfonso Sánchez-Macián"
] |
[
"Anomaly Detection"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/cli-rag-a-retrieval-augmented-framework-for
|
2507.06715
| null | null |
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs
|
Large language models (LLMs), including zero-shot and few-shot paradigms, have shown promising capabilities in clinical text generation. However, real-world applications face two key challenges: (1) patient data is highly unstructured, heterogeneous, and scattered across multiple note types and (2) clinical notes are often long and semantically dense, making naive prompting infeasible due to context length constraints and the risk of omitting clinically relevant information. We introduce CLI-RAG (Clinically Informed Retrieval-Augmented Generation), a domain-specific framework for structured and clinically grounded text generation using LLMs. It incorporates a novel hierarchical chunking strategy that respects clinical document structure and introduces a task-specific dual-stage retrieval mechanism. The global stage identifies relevant note types using evidence-based queries, while the local stage extracts high-value content within those notes creating relevance at both document and section levels. We apply the system to generate structured progress notes for individual hospital visits using 15 clinical note types from the MIMIC-III dataset. Experiments show that it preserves temporal and semantic alignment across visits, achieving an average alignment score of 87.7%, surpassing the 80.7% baseline from real clinician-authored notes. The generated outputs also demonstrate high consistency across LLMs, reinforcing deterministic behavior essential for reproducibility, reliability, and clinical trust.
| null |
https://arxiv.org/abs/2507.06715v1
|
https://arxiv.org/pdf/2507.06715v1.pdf
| null |
[
"Garapati Keerthana",
"Manik Gupta"
] |
[
"Chunking",
"RAG",
"Retrieval",
"Retrieval-augmented Generation",
"Text Generation"
] | 2025-07-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/readme-robust-error-aware-digital-signature
|
2507.04495
| null | null |
README: Robust Error-Aware Digital Signature Framework via Deep Watermarking Model
|
Deep learning-based watermarking has emerged as a promising solution for robust image authentication and protection. However, existing models are limited by low embedding capacity and vulnerability to bit-level errors, making them unsuitable for cryptographic applications such as digital signatures, which require over 2048 bits of error-free data. In this paper, we propose README (Robust Error-Aware Digital Signature via Deep WaterMarking ModEl), a novel framework that enables robust, verifiable, and error-tolerant digital signatures within images. Our method combines a simple yet effective cropping-based capacity scaling mechanism with ERPA (ERror PAinting Module), a lightweight error correction module designed to localize and correct bit errors using Distinct Circular Subsum Sequences (DCSS). Without requiring any fine-tuning of existing pretrained watermarking models, README significantly boosts the zero-bit-error image rate (Z.B.I.R) from 1.2% to 86.3% when embedding 2048-bit digital signatures into a single image, even under real-world distortions. Moreover, our use of perceptual hash-based signature verification ensures public verifiability and robustness against tampering. The proposed framework unlocks a new class of high-assurance applications for deep watermarking, bridging the gap between signal-level watermarking and cryptographic security.
| null |
https://arxiv.org/abs/2507.04495v1
|
https://arxiv.org/pdf/2507.04495v1.pdf
| null |
[
"Hyunwook Choi",
"Sangyun Won",
"Daeyeon Hwang",
"Junhyeok Choi"
] |
[] | 2025-07-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/underwater-monocular-metric-depth-estimation
|
2507.02148
| null | null |
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning
|
Monocular depth estimation has recently advanced to provide not only relative but also metric depth predictions. However, its reliability in underwater environments remains limited due to light attenuation and scattering, color distortion, turbidity, and the lack of high-quality metric ground-truth data. In this paper, we present a comprehensive benchmark of zero-shot and fine-tuned monocular metric depth estimation models on real-world underwater datasets with metric depth annotations, such as FLSea and SQUID. We evaluate a diverse set of state-of-the-art models across a range of underwater conditions with different ranges. Our results show that large-scale models trained on terrestrial (real or synthetic) data, while effective in in-air settings, perform poorly underwater due to significant domain shifts. To address this, we fine-tune Depth Anything V2 with a ViT-S backbone encoder on a synthetic underwater variant of the Hypersim dataset, which we generated using a physically based underwater image formation model. We demonstrate our fine-tuned model consistently improves performance across all benchmarks and outperforms baselines trained only on the clean in-air Hypersim dataset. Our study provides a detailed evaluation and visualization for monocular metric depth estimation in underwater scenes, highlighting the importance of domain adaptation and scale-aware supervision for achieving robust and generalizable metric depth predictions in challenging underwater environments for future research.
| null |
https://arxiv.org/abs/2507.02148v1
|
https://arxiv.org/pdf/2507.02148v1.pdf
| null |
[
"Zijie Cai",
"Christopher Metzler"
] |
[
"Depth Estimation",
"Domain Adaptation",
"Monocular Depth Estimation"
] | 2025-07-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/humanoidgen-data-generation-for-bimanual
|
2507.00833
| null | null |
HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning
|
For robotic manipulation, existing robotics datasets and simulation benchmarks predominantly cater to robot-arm platforms. However, for humanoid robots equipped with dual arms and dexterous hands, simulation tasks and high-quality demonstrations are notably lacking. Bimanual dexterous manipulation is inherently more complex, as it requires coordinated arm movements and hand operations, making autonomous data collection challenging. This paper presents HumanoidGen, an automated task creation and demonstration collection framework that leverages atomic dexterous operations and LLM reasoning to generate relational constraints. Specifically, we provide spatial annotations for both assets and dexterous hands based on the atomic operations, and perform an LLM planner to generate a chain of actionable spatial constraints for arm movements based on object affordances and scenes. To further improve planning ability, we employ a variant of Monte Carlo tree search to enhance LLM reasoning for long-horizon tasks and insufficient annotation. In experiments, we create a novel benchmark with augmented scenarios to evaluate the quality of the collected data. The results show that the performance of the 2D and 3D diffusion policies can scale with the generated dataset. Project page is https://openhumanoidgen.github.io.
| null |
https://arxiv.org/abs/2507.00833v1
|
https://arxiv.org/pdf/2507.00833v1.pdf
| null |
[
"Zhi Jing",
"Siyuan Yang",
"Jicong Ao",
"Ting Xiao",
"Yugang Jiang",
"Chenjia Bai"
] |
[] | 2025-07-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/predicting-graph-structure-via-adapted-flux
|
2507.05806
| null | null |
Predicting Graph Structure via Adapted Flux Balance Analysis
|
Many dynamic processes such as telecommunication and transport networks can be described through discrete time series of graphs. Modelling the dynamics of such time series enables prediction of graph structure at future time steps, which can be used in applications such as detection of anomalies. Existing approaches for graph prediction have limitations such as assuming that the vertices do not to change between consecutive graphs. To address this, we propose to exploit time series prediction methods in combination with an adapted form of flux balance analysis (FBA), a linear programming method originating from biochemistry. FBA is adapted to incorporate various constraints applicable to the scenario of growing graphs. Empirical evaluations on synthetic datasets (constructed via Preferential Attachment model) and real datasets (UCI Message, HePH, Facebook, Bitcoin) demonstrate the efficacy of the proposed approach.
|
Many dynamic processes such as telecommunication and transport networks can be described through discrete time series of graphs.
|
https://arxiv.org/abs/2507.05806v1
|
https://arxiv.org/pdf/2507.05806v1.pdf
| null |
[
"Sevvandi Kandanaarachchi",
"Ziqi Xu",
"Stefan Westerlund",
"Conrad Sanderson"
] |
[
"Prediction",
"Time Series",
"Time Series Prediction"
] | 2025-07-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/infinity-parser-layout-aware-reinforcement
|
2506.03197
| null | null |
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing
|
Automated parsing of scanned documents into richly structured, machine-readable formats remains a critical bottleneck in Document AI, as traditional multi-stage pipelines suffer from error propagation and limited adaptability to diverse layouts. We introduce layoutRL, an end-to-end reinforcement learning framework that trains models to be explicitly layout-aware by optimizing a composite reward of normalized edit distance, paragraph count accuracy, and reading order preservation. Leveraging our newly released dataset, Infinity-Doc-55K, which combines 55K high-fidelity synthetic scanned document parsing data with expert-filtered real-world documents, we instantiate layoutRL in a vision-language-model-based parser called Infinity-Parser. Evaluated on English and Chinese benchmarks for OCR, table and formula extraction, and reading order detection, Infinity-Parser achieves new state-of-the-art performance in both accuracy and structural fidelity, outpacing specialist pipelines and general-purpose vision-language models. We will publicly release our code and dataset to accelerate progress in robust document understanding.
|
Automated parsing of scanned documents into richly structured, machine-readable formats remains a critical bottleneck in Document AI, as traditional multi-stage pipelines suffer from error propagation and limited adaptability to diverse layouts.
|
https://arxiv.org/abs/2506.03197v1
|
https://arxiv.org/pdf/2506.03197v1.pdf
| null |
[
"Baode Wang",
"Biao Wu",
"Weizhen Li",
"Meng Fang",
"Yanjie Liang",
"Zuming Huang",
"Haozhe Wang",
"Jun Huang",
"Ling Chen",
"Wei Chu",
"Yuan Qi"
] |
[
"Document AI",
"document understanding",
"Language Modeling",
"Language Modelling",
"Optical Character Recognition (OCR)",
"Reading Order Detection",
"reinforcement-learning",
"Reinforcement Learning"
] | 2025-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pre-3-enabling-deterministic-pushdown
|
2506.03887
| null | null |
Pre$^3$: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation
|
Extensive LLM applications demand efficient structured generations, particularly for LR(1) grammars, to produce outputs in specified formats (e.g., JSON). Existing methods primarily parse LR(1) grammars into a pushdown automaton (PDA), leading to runtime execution overhead for context-dependent token processing, especially inefficient under large inference batches. To address these issues, we propose Pre$^3$ that exploits deterministic pushdown automata (DPDA) to optimize the constrained LLM decoding efficiency. First, by precomputing prefix-conditioned edges during the preprocessing, Pre$^3$ enables ahead-of-time edge analysis and thus makes parallel transition processing possible. Second, by leveraging the prefix-conditioned edges, Pre$^3$ introduces a novel approach that transforms LR(1) transition graphs into DPDA, eliminating the need for runtime path exploration and achieving edge transitions with minimal overhead. Pre$^3$ can be seamlessly integrated into standard LLM inference frameworks, reducing time per output token (TPOT) by up to 40% and increasing throughput by up to 36% in our experiments. Our code is available at https://github.com/ModelTC/lightllm.
|
Second, by leveraging the prefix-conditioned edges, Pre$^3$ introduces a novel approach that transforms LR(1) transition graphs into DPDA, eliminating the need for runtime path exploration and achieving edge transitions with minimal overhead.
|
https://arxiv.org/abs/2506.03887v1
|
https://arxiv.org/pdf/2506.03887v1.pdf
| null |
[
"Junyi Chen",
"Shihao Bai",
"Zaijun Wang",
"Siyu Wu",
"Chuheng Du",
"Hailong Yang",
"Ruihao Gong",
"Shengzhong Liu",
"Fan Wu",
"Guihai Chen"
] |
[] | 2025-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-survey-of-continual-reinforcement-learning
|
2506.21872
| null | null |
A Survey of Continual Reinforcement Learning
|
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field due to the rapid development of deep neural networks. However, the success of RL currently relies on extensive training data and computational resources. In addition, RL's limited ability to generalize across tasks restricts its applicability in dynamic and real-world environments. With the arisen of Continual Learning (CL), Continual Reinforcement Learning (CRL) has emerged as a promising research direction to address these limitations by enabling agents to learn continuously, adapt to new tasks, and retain previously acquired knowledge. In this survey, we provide a comprehensive examination of CRL, focusing on its core concepts, challenges, and methodologies. Firstly, we conduct a detailed review of existing works, organizing and analyzing their metrics, tasks, benchmarks, and scenario settings. Secondly, we propose a new taxonomy of CRL methods, categorizing them into four types from the perspective of knowledge storage and/or transfer. Finally, our analysis highlights the unique challenges of CRL and provides practical insights into future directions.
| null |
https://arxiv.org/abs/2506.21872v1
|
https://arxiv.org/pdf/2506.21872v1.pdf
| null |
[
"Chaofan Pan",
"Xin Yang",
"Yanhua Li",
"Wei Wei",
"Tianrui Li",
"Bo An",
"Jiye Liang"
] |
[
"Continual Learning",
"Decision Making",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Sequential Decision Making",
"Survey"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/advancements-and-challenges-in-continual
|
2506.21899
| null | null |
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
|
The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuous reinforcement learning. This survey reviews how continual learning transforms RL agents into dynamic continual learners. This enables RL agents to acquire and retain useful and reusable knowledge seamlessly. The paper delves into fundamental aspects of continual reinforcement learning, exploring key concepts, significant challenges, and novel methodologies. Special emphasis is placed on recent advancements in continual reinforcement learning within robotics, along with a succinct overview of evaluation environments utilized in prominent research, facilitating accessibility for newcomers to the field. The review concludes with a discussion on limitations and promising future directions, providing valuable insights for researchers and practitioners alike.
| null |
https://arxiv.org/abs/2506.21899v1
|
https://arxiv.org/pdf/2506.21899v1.pdf
| null |
[
"Amara Zuffer",
"Michael Burke",
"Mehrtash Harandi"
] |
[
"Continual Learning",
"Diversity",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2025-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/swe-bench-cl-continual-learning-for-coding
|
2507.00014
| null | null |
SWE-Bench-CL: Continual Learning for Coding Agents
|
Large Language Models (LLMs) have achieved impressive results on static code-generation benchmarks, but real-world software development unfolds as a continuous stream of evolving issues, fixes, and feature requests. We introduce SWE-Bench-CL, a novel continual learning benchmark built on the human-verified SWE-Bench Verified dataset introduced by OpenAI and Princeton-NLP in 2024. By organizing GitHub issues into chronologically ordered sequences that reflect natural repository evolution, SWE-Bench-CL enables direct evaluation of an agent's ability to accumulate experience, transfer knowledge across tasks, and resist catastrophic forgetting. We complement the dataset with (i) a preliminary analysis of inter-task structural similarity and contextual sensitivity, (ii) an interactive LangGraph-based evaluation framework augmented with a FAISS-backed semantic memory module, and (iii) a suite of specialized continual learning metrics -- including average accuracy, forgetting, forward/backward transfer, tool-use efficiency, and a generalized Composite Continual Learning Score and CL-F-beta score -- to capture the stability-plasticity trade-off. We outline a rigorous experimental protocol comparing memory-enabled and memory-disabled agents across diverse Python repositories. All code and data are publicly available at https://github.com/thomasjoshi/agents-never-forget, providing the community with a reproducible platform for developing more adaptive and robust AI agents in software engineering.
|
Large Language Models (LLMs) have achieved impressive results on static code-generation benchmarks, but real-world software development unfolds as a continuous stream of evolving issues, fixes, and feature requests.
|
https://arxiv.org/abs/2507.00014v1
|
https://arxiv.org/pdf/2507.00014v1.pdf
| null |
[
"Thomas Joshi",
"Shayan Chowdhury",
"Fatih Uysal"
] |
[
"Code Generation",
"Continual Learning"
] | 2025-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/denseworld-1m-towards-detailed-dense-grounded
|
2506.24102
| null | null |
DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World
|
Multimodal Large Language Models (MLLMs) demonstrate a complex understanding of scenes, benefiting from large-scale and high-quality datasets. Most existing caption datasets lack the ground locations and relations for visual entities. Several grounded caption datasets face the problems of missing detailed descriptions, relations, and massive object descriptions on high-resolution images. To fill this gap for the community, we present DenseWorld-1M, the first massive, detailed, dense grounded caption dataset in the real world. We design a three-stage labeling pipeline, containing open-world perception, detailed object caption generation, and dense caption merging. The first stage obtains entity-level masks and labels. The second stage generates the object-level, detailed captions with the guidance of masks and labels from the first stage. The final stage merges object captions and masks into spatial and relational dense captions. To accelerate the labeling process and improve caption quality, we present two VLM models: the Detailed Region Caption model and the Spatial Caption Merging model. Extensive experiments on various settings, including vision-language understanding, visual grounding, and region caption generation, demonstrate the effectiveness of our DenseWorld-1M dataset and labeling models.
|
Extensive experiments on various settings, including vision-language understanding, visual grounding, and region caption generation, demonstrate the effectiveness of our DenseWorld-1M dataset and labeling models.
|
https://arxiv.org/abs/2506.24102v1
|
https://arxiv.org/pdf/2506.24102v1.pdf
| null |
[
"Xiangtai Li",
"Tao Zhang",
"Yanwei Li",
"Haobo Yuan",
"Shihao Chen",
"Yikang Zhou",
"Jiahao Meng",
"Yueyi Sun",
"Shilin Xu",
"Lu Qi",
"Tianheng Cheng",
"Yi Lin",
"Zilong Huang",
"Wenhao Huang",
"Jiashi Feng",
"Guang Shi"
] |
[
"Caption Generation",
"Object",
"Visual Grounding"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-future-is-agentic-definitions
|
2507.02097
| null | null |
The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
|
Large language models (LLMs) are rapidly evolving from passive engines of text generation into agentic entities that can plan, remember, invoke external tools, and co-operate with one another. This perspective paper investigates how such LLM agents (and societies thereof) can transform the design space of recommender systems. We introduce a unified formalism that (i) models an individual agent as a tuple comprising its language core, tool set, and hierarchical memory, and (ii) captures a multi-agent recommender as a triple of agents, shared environment, and communication protocol. Within this framework, we present four end-to-end use cases-interactive party planning, synthetic user-simulation for offline evaluation, multi-modal furniture recommendation, and brand-aligned explanation generation-each illustrating a distinct capability unlocked by agentic orchestration. We then surface five cross-cutting challenge families: protocol complexity, scalability, hallucination and error propagation, emergent misalignment (including covert collusion), and brand compliance. For each, we formalize the problem, review nascent mitigation strategies, and outline open research questions. The result is both a blueprint and an agenda: a blueprint that shows how memory-augmented, tool-using LLM agents can be composed into robust recommendation pipelines, and an agenda inviting the RecSys community to develop benchmarks, theoretical guarantees, and governance tools that keep pace with this new degree of autonomy. By unifying agentic abstractions with recommender objectives, the paper lays the groundwork for the next generation of personalized, trustworthy, and context-rich recommendation services.
| null |
https://arxiv.org/abs/2507.02097v2
|
https://arxiv.org/pdf/2507.02097v2.pdf
| null |
[
"Reza Yousefi Maragheh",
"Yashar Deldjoo"
] |
[
"Explanation Generation",
"Hallucination",
"Recommendation Systems",
"Text Generation",
"User Simulation"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/llm2rec-large-language-models-are-powerful
|
2506.21579
| null | null |
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
|
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions. While these methods enhance generalization, they fail to encode CF signals-i.e., latent item correlations and preference patterns-crucial for effective recommendation. We argue that an ideal embedding model should seamlessly integrate CF signals with rich semantic representations to improve both in-domain and out-of-domain recommendation performance. To this end, we propose LLM2Rec, a novel embedding model tailored for sequential recommendation, integrating the rich semantic understanding of LLMs with CF awareness. Our approach follows a two-stage training framework: (1) Collaborative Supervised Fine-tuning, which adapts LLMs to infer item relationships based on historical interactions, and (2) Item-level Embedding Modeling, which refines these specialized LLMs into structured item embedding models that encode both semantic and collaborative information. Extensive experiments on real-world datasets demonstrate that LLM2Rec effectively improves recommendation quality across both in-domain and out-of-domain settings. Our findings highlight the potential of leveraging LLMs to build more robust, generalizable embedding models for sequential recommendation. Our codes are available at https://github.com/HappyPointer/LLM2Rec.
|
To this end, we propose LLM2Rec, a novel embedding model tailored for sequential recommendation, integrating the rich semantic understanding of LLMs with CF awareness.
|
https://arxiv.org/abs/2506.21579v1
|
https://arxiv.org/pdf/2506.21579v1.pdf
| null |
[
"Yingzhi He",
"Xiaohao Liu",
"An Zhang",
"Yunshan Ma",
"Tat-Seng Chua"
] |
[
"Collaborative Filtering",
"Sequential Recommendation"
] | 2025-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/refine-poi-reinforcement-fine-tuned-large
|
2506.21599
| null | null |
Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
|
Large language models (LLMs) have been adopted for next point-of-interest (POI) recommendation tasks. Typical LLM-based recommenders fall into two categories: prompt-based and supervised fine-tuning (SFT)-based models. Prompt-based models generally offer greater output flexibility but deliver lower accuracy, whereas SFT-based models achieve higher performance yet face a fundamental mismatch: next POI recommendation data does not naturally suit supervised fine-tuning. In SFT, the model is trained to reproduce the exact ground truth, but each training example provides only a single target POI, so there is no ground truth for producing a top-k list. To address this, we propose Refine-POI, a reinforcement fine-tuning framework for next POI recommendation. We introduce recommendation-driven rewards that enable LLMs to learn to generate top-k recommendation lists using only one ground-truth POI per example. Experiments on real-world datasets demonstrate that Refine-POI achieves state-of-the-art top-k recommendation performance.
| null |
https://arxiv.org/abs/2506.21599v2
|
https://arxiv.org/pdf/2506.21599v2.pdf
| null |
[
"Peibo Li",
"Shuang Ao",
"Hao Xue",
"Yang song",
"Maarten de Rijke",
"Johan Barthélemy",
"Tomasz Bednarz",
"Flora D. Salim"
] |
[] | 2025-06-19T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Shrink and Fine-Tune**, or **SFT**, is a type of distillation that avoids explicit distillation by copying parameters to a student student model and then fine-tuning. Specifically it extracts a student model from the maximally spaced layers of a fine-tuned teacher. Each layer $l \\in L'$ is copied fully from $L$. For example, when creating a [BART](https://paperswithcode.com/method/bart) student with 3 decoder layers from the 12 encoder layer 12 decoder layer teacher, we copy the teacher’s full $Enc^{L}$ and decoder layers 0, 6, and 11 to the student. When deciding which layers to copy, we break ties arbitrarily; copying layers 0, 5, and 11 might work just as well. When copy only 1 decoder layer, we copy layer 0. This was found this to work better than copying layer 11. The impact of initialization on performance is measured experimentally in Section 6.1. After initialization, the student model continues to fine-tune on the summarization dataset, with the objective of minimizing $\\mathcal{L}\\_{Data}$.",
"full_name": "Shrink and Fine-Tune",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Knowledge Distillation",
"parent": null
},
"name": "SFT",
"source_title": "Pre-trained Summarization Distillation",
"source_url": "https://arxiv.org/abs/2010.13002v2"
}
] |
https://paperswithcode.com/paper/iranker-towards-ranking-foundation-model
|
2506.21638
| null | null |
IRanker: Towards Ranking Foundation Model
|
Ranking tasks are ubiquitous, encompassing applications such as recommendation systems, LLM routing, and item re-ranking. We propose to unify these tasks using a single ranking foundation model (FM), as it eliminates the need for designing different models for each specific ranking task. However, unlike general supervision tasks in LLMs, ranking tasks do not have clear labels for supervision, posing great challenges to developing a ranking FM. To overcome these challenges, we propose IRanker, a ranking FM framework with reinforcement learning (RL) and iterative decoding. Our insight is to decompose the complex ranking task into an iterative decoding process that eliminates the worst candidate from the candidate pool step by step, which significantly reduces the output combinatorial space and better utilizes the limited context length during RL training. We meticulously train and comprehensively evaluate an IRanker-3B model on nine datasets across three scenarios: recommendation, routing, and passage ranking. The results show that a single IRanker-3B achieves state-of-the-art results on several datasets compared to models of similar size, and even surpasses the performance of larger models on certain datasets. We further demonstrate the effectiveness of our RL design and the robustness of the iterative mechanism across different LLM sizes. Moreover, we conducted both in-domain and out-of-domain zero-shot generalization experiments, which showed that IRanker-3B achieved good generalization on in-domain ranking tasks compared to the base LLM by at least 5% improvement. Surprisingly, on out-of-domain generic LLM tasks, IRanker-3B outperformed the base model by at least 9% on GSM8K, IFEval, and MathQA. In addition, the thoughts generated by IRanker-3B during training could further enhance zero-shot LLM performance.
|
We propose to unify these tasks using a single ranking foundation model (FM), as it eliminates the need for designing different models for each specific ranking task.
|
https://arxiv.org/abs/2506.21638v1
|
https://arxiv.org/pdf/2506.21638v1.pdf
| null |
[
"Tao Feng",
"Zhigang Hua",
"Zijie Lei",
"Yan Xie",
"Shuang Yang",
"Bo Long",
"Jiaxuan You"
] |
[
"GSM8K",
"model",
"Passage Ranking",
"Recommendation Systems",
"Reinforcement Learning (RL)",
"Re-Ranking",
"Zero-shot Generalization"
] | 2025-06-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] |
https://paperswithcode.com/paper/findrec-stein-guided-entropic-flow-for-multi
|
2507.04651
| null | null |
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
|
Modern recommendation systems face significant challenges in processing multimodal sequential data, particularly in temporal dynamics modeling and information flow coordination. Traditional approaches struggle with distribution discrepancies between heterogeneous features and noise interference in multimodal signals. We propose \textbf{FindRec}~ (\textbf{F}lexible unified \textbf{in}formation \textbf{d}isentanglement for multi-modal sequential \textbf{Rec}ommendation), introducing a novel "information flow-control-output" paradigm. The framework features two key innovations: (1) A Stein kernel-based Integrated Information Coordination Module (IICM) that theoretically guarantees distribution consistency between multimodal features and ID streams, and (2) A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance. Our approach leverages multi-head subspace decomposition for routing stability and RBF-Stein gradient for unbiased distribution alignment, enhanced by linear-complexity Mamba layers for efficient temporal modeling. Extensive experiments on three real-world datasets demonstrate FindRec's superior performance over state-of-the-art baselines, particularly in handling long sequences and noisy multimodal inputs. Our framework achieves both improved recommendation accuracy and enhanced model interpretability through its modular design. The implementation code is available anonymously online for easy reproducibility~\footnote{https://github.com/Applied-Machine-Learning-Lab/FindRec}.
|
Modern recommendation systems face significant challenges in processing multimodal sequential data, particularly in temporal dynamics modeling and information flow coordination.
|
https://arxiv.org/abs/2507.04651v1
|
https://arxiv.org/pdf/2507.04651v1.pdf
| null |
[
"Maolin Wang",
"Yutian Xiao",
"Binhao Wang",
"Sheng Zhang",
"Shanshan Ye",
"Wanyu Wang",
"Hongzhi Yin",
"Ruocheng Guo",
"Zenglin Xu"
] |
[
"Mamba",
"Recommendation Systems",
"Sequential Recommendation"
] | 2025-07-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/state-spaces/mamba",
"description": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pre-training and downstream evaluation.",
"full_name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"introduced_year": 2000,
"main_collection": null,
"name": "Mamba",
"source_title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"source_url": "https://arxiv.org/abs/2312.00752v2"
}
] |
https://paperswithcode.com/paper/heterogeneous-user-modeling-for-llm-based
|
2507.04626
| null | null |
Heterogeneous User Modeling for LLM-based Recommendation
|
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation. A key challenge to advancing open-domain recommendation lies in effectively modeling user preferences from users' heterogeneous behaviors across multiple domains. Existing approaches, including ID-based and semantic-based modeling, struggle with poor generalization, an inability to compress noisy interactions effectively, and the domain seesaw phenomenon. To address these challenges, we propose a Heterogeneous User Modeling (HUM) method, which incorporates a compression enhancer and a robustness enhancer for LLM-based recommendation. The compression enhancer uses a customized prompt to compress heterogeneous behaviors into a tailored token, while a masking mechanism enhances cross-domain knowledge extraction and understanding. The robustness enhancer introduces a domain importance score to mitigate the domain seesaw phenomenon by guiding domain optimization. Extensive experiments on heterogeneous datasets validate that HUM effectively models user heterogeneity by achieving both high efficacy and robustness, leading to superior performance in open-domain recommendation.
|
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation.
|
https://arxiv.org/abs/2507.04626v1
|
https://arxiv.org/pdf/2507.04626v1.pdf
| null |
[
"Honghui Bao",
"Wenjie Wang",
"Xinyu Lin",
"Fengbin Zhu",
"Teng Sun",
"Fuli Feng",
"Tat-Seng Chua"
] |
[] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bifair-a-fairness-aware-training-framework
|
2507.04294
| null | null |
BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
|
Large Language Model-enhanced Recommender Systems (LLM-enhanced RSs) have emerged as a powerful approach to improving recommendation quality by leveraging LLMs to generate item representations. Despite these advancements, the integration of LLMs raises severe fairness concerns. Existing studies reveal that LLM-based RSs exhibit greater unfairness than traditional RSs, yet fairness issues in LLM-enhanced RSs remain largely unexplored. In this paper, our empirical study reveals that while LLM-enhanced RSs improve fairness across item groups, a significant fairness gap persists. Further enhancement remains challenging due to the architectural differences and varying sources of unfairness inherent in LLM-enhanced RSs. To bridge this gap, we first decompose unfairness into i) \textit{prior unfairness} in LLM-generated representations and ii) \textit{training unfairness} in recommendation models. Then, we propose BiFair, a bi-level optimization-based fairness-aware training framework designed to mitigate both prior and training unfairness simultaneously. BiFair optimizes two sets of learnable parameters: LLM-generated representations and a trainable projector in the recommendation model, using a two-level nested optimization process. Additionally, we introduce an adaptive inter-group balancing mechanism, leveraging multi-objective optimization principles to dynamically balance fairness across item groups. Extensive experiments on three real-world datasets demonstrate that BiFair significantly mitigates unfairness and outperforms previous state-of-the-art methods.
| null |
https://arxiv.org/abs/2507.04294v1
|
https://arxiv.org/pdf/2507.04294v1.pdf
| null |
[
"Jiaming Zhang",
"Yuyuan Li",
"Yiqun Xu",
"Li Zhang",
"Xiaohua Feng",
"Zhifei Ren",
"Chaochao Chen"
] |
[
"Fairness",
"Large Language Model",
"Recommendation Systems"
] | 2025-07-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ctr-guided-generative-query-suggestion-in
|
2507.04072
| null | null |
CTR-Guided Generative Query Suggestion in Conversational Search
|
Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. We propose GQS, a generative framework that integrates click modeling and preference optimization to enhance real-world user engagement. GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a CTR-Calibrated Iterative Optimization process that jointly refines the CTR and generation models across training rounds. Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.
| null |
https://arxiv.org/abs/2507.04072v1
|
https://arxiv.org/pdf/2507.04072v1.pdf
| null |
[
"Erxue Min",
"Hsiu-Yuan Huang",
"Xihong Yang",
"Min Yang",
"Xin Jia",
"Yunfang Wu",
"Hengyi Cai",
"Junfeng Wang",
"Shuaiqiang Wang",
"Dawei Yin"
] |
[
"Conversational Search",
"Diversity"
] | 2025-07-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scaling-up-biomedical-vision-language-models
|
2505.17436
| null | null |
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning
|
To advance biomedical vison-language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handling long text, explore strategies to efficiently adopt vision language models for diverse multi-modal biomedical tasks, and examine the zero-shot learning performance. We developed two biomedical vision language models, BiomedGPT-Large and BiomedGPT-XLarge, based on an encoder-decoder-based transformer architecture. We fine-tuned the two models on 23 benchmark datasets from 6 multi-modal biomedical tasks including one image-only task (image classification), three language-only tasks (text understanding, text summarization and question answering), and two vision-language tasks (visual question answering and image captioning). We compared the developed scaled models with our previous BiomedGPT-Base model and existing prestigious models reported in the literature. We instruction-tuned the two models using a large-scale multi-modal biomedical instruction-tuning dataset and assessed the zero-shot learning performance and alignment accuracy.
|
To advance biomedical vison-language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handling long text, explore strategies to efficiently adopt vision language models for diverse multi-modal biomedical tasks, and examine the zero-shot learning performance.
|
https://arxiv.org/abs/2505.17436v1
|
https://arxiv.org/pdf/2505.17436v1.pdf
| null |
[
"Cheng Peng",
"Kai Zhang",
"Mengxian Lyu",
"Hongfang Liu",
"Lichao Sun",
"Yonghui Wu"
] |
[
"Decoder",
"Image Captioning",
"image-classification",
"Image Classification",
"Language Modeling",
"Language Modelling",
"Question Answering",
"Text Summarization",
"Visual Question Answering",
"Zero-Shot Learning"
] | 2025-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "ADaptive gradient method with the OPTimal convergence rate",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "ADOPT",
"source_title": "ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate",
"source_url": "https://arxiv.org/abs/2411.02853v3"
}
] |
https://paperswithcode.com/paper/rsrefseg-2-decoupling-referring-remote
|
2507.06231
| null | null |
RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
|
Referring Remote Sensing Image Segmentation provides a flexible and fine-grained framework for remote sensing scene analysis via vision-language collaborative interpretation. Current approaches predominantly utilize a three-stage pipeline encompassing dual-modal encoding, cross-modal interaction, and pixel decoding. These methods demonstrate significant limitations in managing complex semantic relationships and achieving precise cross-modal alignment, largely due to their coupled processing mechanism that conflates target localization with boundary delineation. This architectural coupling amplifies error propagation under semantic ambiguity while restricting model generalizability and interpretability. To address these issues, we propose RSRefSeg 2, a decoupling paradigm that reformulates the conventional workflow into a collaborative dual-stage framework: coarse localization followed by fine segmentation. RSRefSeg 2 integrates CLIP's cross-modal alignment strength with SAM's segmentation generalizability through strategic foundation model collaboration. Specifically, CLIP is employed as the dual-modal encoder to activate target features within its pre-aligned semantic space and generate localization prompts. To mitigate CLIP's misactivation challenges in multi-entity scenarios described by referring texts, a cascaded second-order prompter is devised, which enhances precision through implicit reasoning via decomposition of text embeddings into complementary semantic subspaces. These optimized semantic prompts subsequently direct the SAM to generate pixel-level refined masks, thereby completing the semantic transmission pipeline. Extensive experiments (RefSegRS, RRSIS-D, and RISBench) demonstrate that RSRefSeg 2 surpasses contemporary methods in segmentation accuracy (+~3% gIoU) and complex semantic interpretation. Code is available at: https://github.com/KyanChen/RSRefSeg2.
|
Referring Remote Sensing Image Segmentation provides a flexible and fine-grained framework for remote sensing scene analysis via vision-language collaborative interpretation.
|
https://arxiv.org/abs/2507.06231v1
|
https://arxiv.org/pdf/2507.06231v1.pdf
| null |
[
"Keyan Chen",
"Chenyang Liu",
"Bowen Chen",
"Jiafan Zhang",
"Zhengxia Zou",
"Zhenwei Shi"
] |
[
"cross-modal alignment",
"Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Segment Anything Model",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Segmentation Models",
"parent": null
},
"name": "SAM",
"source_title": "Segment Anything",
"source_url": "https://arxiv.org/abs/2304.02643v1"
},
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
}
] |
https://paperswithcode.com/paper/rethinking-layered-graphic-design-generation
|
2507.05601
| null | null |
Rethinking Layered Graphic Design Generation with a Top-Down Approach
|
Graphic design is crucial for conveying ideas and messages. Designers usually organize their work into objects, backgrounds, and vectorized text layers to simplify editing. However, this workflow demands considerable expertise. With the rise of GenAI methods, an endless supply of high-quality graphic designs in pixel format has become more accessible, though these designs often lack editability. Despite this, non-layered designs still inspire human designers, influencing their choices in layouts and text styles, ultimately guiding the creation of layered designs. Motivated by this observation, we propose Accordion, a graphic design generation framework taking the first attempt to convert AI-generated designs into editable layered designs, meanwhile refining nonsensical AI-generated text with meaningful alternatives guided by user prompts. It is built around a vision language model (VLM) playing distinct roles in three curated stages. For each stage, we design prompts to guide the VLM in executing different tasks. Distinct from existing bottom-up methods (e.g., COLE and Open-COLE) that gradually generate elements to create layered designs, our approach works in a top-down manner by using the visually harmonious reference image as global guidance to decompose each layer. Additionally, it leverages multiple vision experts such as SAM and element removal models to facilitate the creation of graphic layers. We train our method using the in-house graphic design dataset Design39K, augmented with AI-generated design images coupled with refined ground truth created by a customized inpainting model. Experimental results and user studies by designers show that Accordion generates favorable results on the DesignIntention benchmark, including tasks such as text-to-template, adding text to background, and text de-rendering, and also excels in creating design variations.
| null |
https://arxiv.org/abs/2507.05601v1
|
https://arxiv.org/pdf/2507.05601v1.pdf
| null |
[
"Jingye Chen",
"Zhaowen Wang",
"Nanxuan Zhao",
"Li Zhang",
"Difan Liu",
"Jimei Yang",
"Qifeng Chen"
] |
[] | 2025-07-08T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Accordion** is a gradient communication scheduling algorithm that is generic across models while imposing low computational overheads. Accordion inspects the change in the gradient norms to detect critical regimes and adjusts the communication schedule dynamically. Accordion works for both adjusting the gradient compression rate or the batch size without additional parameter tuning.",
"full_name": "Accordion",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "This section contains a compilation of distributed methods for scaling deep learning to very large models. There are many different strategies for scaling training across multiple devices, including:\r\n\r\n - [Data Parallel](https://paperswithcode.com/methods/category/data-parallel-methods) : for each node we use the same model parameters to do forward propagation, but we send a small batch of different data to each node, compute the gradient normally, and send it back to the main node. Once we have all the gradients, we calculate the weighted average and use this to update the model parameters.\r\n\r\n - [Model Parallel](https://paperswithcode.com/methods/category/model-parallel-methods) : for each node we assign different layers to it. During forward propagation, we start in the node with the first layers, then move onto the next, and so on. Once forward propagation is done we calculate gradients for the last node, and update model parameters for that node. Then we backpropagate onto the penultimate node, update the parameters, and so on.\r\n\r\n - Additional methods including [Hybrid Parallel](https://paperswithcode.com/methods/category/hybrid-parallel-methods), [Auto Parallel](https://paperswithcode.com/methods/category/auto-parallel-methods), and [Distributed Communication](https://paperswithcode.com/methods/category/distributed-communication).\r\n\r\nImage credit: [Jordi Torres](https://towardsdatascience.com/scalable-deep-learning-on-parallel-and-distributed-infrastructures-e5fb4a956bef).",
"name": "Distributed Methods",
"parent": null
},
"name": "Accordion",
"source_title": "Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification",
"source_url": "https://arxiv.org/abs/2010.16248v1"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Segment Anything Model",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Segmentation Models",
"parent": null
},
"name": "SAM",
"source_title": "Segment Anything",
"source_url": "https://arxiv.org/abs/2304.02643v1"
},
{
"code_snippet_url": "",
"description": "Train a convolutional neural network to generate the contents of an arbitrary image region conditioned on its surroundings.",
"full_name": "Inpainting",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Self-Supervised Learning** refers to a category of methods where we learn representations in a self-supervised way (i.e without labels). These methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. Below you can find a continuously updating list of self-supervised methods.",
"name": "Self-Supervised Learning",
"parent": null
},
"name": "Inpainting",
"source_title": "Context Encoders: Feature Learning by Inpainting",
"source_url": "http://arxiv.org/abs/1604.07379v2"
}
] |
https://paperswithcode.com/paper/openworldsam-extending-sam2-for-universal
|
2507.05427
| null | null |
OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts
|
The ability to segment objects based on open-ended language prompts remains a critical challenge, requiring models to ground textual semantics into precise spatial masks while handling diverse and unseen categories. We present OpenWorldSAM, a framework that extends the prompt-driven Segment Anything Model v2 (SAM2) to open-vocabulary scenarios by integrating multi-modal embeddings extracted from a lightweight vision-language model (VLM). Our approach is guided by four key principles: i) Unified prompting: OpenWorldSAM supports a diverse range of prompts, including category-level and sentence-level language descriptions, providing a flexible interface for various segmentation tasks. ii) Efficiency: By freezing the pre-trained components of SAM2 and the VLM, we train only 4.5 million parameters on the COCO-stuff dataset, achieving remarkable resource efficiency. iii) Instance Awareness: We enhance the model's spatial understanding through novel positional tie-breaker embeddings and cross-attention layers, enabling effective segmentation of multiple instances. iv) Generalization: OpenWorldSAM exhibits strong zero-shot capabilities, generalizing well on unseen categories and an open vocabulary of concepts without additional training. Extensive experiments demonstrate that OpenWorldSAM achieves state-of-the-art performance in open-vocabulary semantic, instance, and panoptic segmentation across multiple benchmarks, including ADE20k, PASCAL, ScanNet, and SUN-RGBD.
| null |
https://arxiv.org/abs/2507.05427v1
|
https://arxiv.org/pdf/2507.05427v1.pdf
| null |
[
"Shiting Xiao",
"Rishabh Kabra",
"Yuhang Li",
"DongHyun Lee",
"Joao Carreira",
"Priyadarshini Panda"
] |
[
"Image Segmentation",
"Panoptic Segmentation",
"Semantic Segmentation"
] | 2025-07-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/samed-2-selective-memory-enhanced-medical
|
2507.03698
| null | null |
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
|
Recent "segment anything" efforts show promise by learning from large-scale data, but adapting such models directly to medical images remains challenging due to the complexity of medical data, noisy annotations, and continual learning requirements across diverse modalities and anatomical structures. In this work, we propose SAMed-2, a new foundation model for medical image segmentation built upon the SAM-2 architecture. Specifically, we introduce a temporal adapter into the image encoder to capture image correlations and a confidence-driven memory mechanism to store high-certainty features for later retrieval. This memory-based strategy counters the pervasive noise in large-scale medical datasets and mitigates catastrophic forgetting when encountering new tasks or modalities. To train and evaluate SAMed-2, we curate MedBank-100k, a comprehensive dataset spanning seven imaging modalities and 21 medical segmentation tasks. Our experiments on both internal benchmarks and 10 external datasets demonstrate superior performance over state-of-the-art baselines in multi-task scenarios. The code is available at: https://github.com/ZhilingYan/Medical-SAM-Bench.
|
In this work, we propose SAMed-2, a new foundation model for medical image segmentation built upon the SAM-2 architecture.
|
https://arxiv.org/abs/2507.03698v1
|
https://arxiv.org/pdf/2507.03698v1.pdf
| null |
[
"Zhiling Yan",
"Sifan Song",
"Dingjie Song",
"Yiwei Li",
"Rong Zhou",
"Weixiang Sun",
"Zhennong Chen",
"Sekeun Kim",
"Hui Ren",
"Tianming Liu",
"Quanzheng Li",
"Xiang Li",
"Lifang He",
"Lichao Sun"
] |
[
"Continual Learning",
"Image Segmentation",
"Medical Image Segmentation",
"Semantic Segmentation"
] | 2025-07-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Adapter",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Adapter",
"source_title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing",
"source_url": "https://arxiv.org/abs/2101.03289v5"
}
] |
https://paperswithcode.com/paper/causal-sam-llm-large-language-models-as
|
2507.03585
| null | null |
Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation
|
The clinical utility of deep learning models for medical image segmentation is severely constrained by their inability to generalize to unseen domains. This failure is often rooted in the models learning spurious correlations between anatomical content and domain-specific imaging styles. To overcome this fundamental challenge, we introduce Causal-SAM-LLM, a novel framework that elevates Large Language Models (LLMs) to the role of causal reasoners. Our framework, built upon a frozen Segment Anything Model (SAM) encoder, incorporates two synergistic innovations. First, Linguistic Adversarial Disentanglement (LAD) employs a Vision-Language Model to generate rich, textual descriptions of confounding image styles. By training the segmentation model's features to be contrastively dissimilar to these style descriptions, it learns a representation robustly purged of non-causal information. Second, Test-Time Causal Intervention (TCI) provides an interactive mechanism where an LLM interprets a clinician's natural language command to modulate the segmentation decoder's features in real-time, enabling targeted error correction. We conduct an extensive empirical evaluation on a composite benchmark from four public datasets (BTCV, CHAOS, AMOS, BraTS), assessing generalization under cross-scanner, cross-modality, and cross-anatomy settings. Causal-SAM-LLM establishes a new state of the art in out-of-distribution (OOD) robustness, improving the average Dice score by up to 6.2 points and reducing the Hausdorff Distance by 15.8 mm over the strongest baseline, all while using less than 9% of the full model's trainable parameters. Our work charts a new course for building robust, efficient, and interactively controllable medical AI systems.
| null |
https://arxiv.org/abs/2507.03585v1
|
https://arxiv.org/pdf/2507.03585v1.pdf
| null |
[
"Tao Tang",
"Shijie Xu",
"Yiting Wu",
"Zhixiang Lu"
] |
[
"Anatomy",
"Disentanglement",
"Image Segmentation",
"Medical Image Segmentation",
"Semantic Segmentation"
] | 2025-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/weakly-supervised-contrastive-learning-with
|
2507.02454
| null | null |
Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection
|
Different from general object detection, moving infrared small target detection faces huge challenges due to tiny target size and weak background contrast.Currently, most existing methods are fully-supervised, heavily relying on a large number of manual target-wise annotations. However, manually annotating video sequences is often expensive and time-consuming, especially for low-quality infrared frame images. Inspired by general object detection, non-fully supervised strategies ($e.g.$, weakly supervised) are believed to be potential in reducing annotation requirements. To break through traditional fully-supervised frameworks, as the first exploration work, this paper proposes a new weakly-supervised contrastive learning (WeCoL) scheme, only requires simple target quantity prompts during model training.Specifically, in our scheme, based on the pretrained segment anything model (SAM), a potential target mining strategy is designed to integrate target activation maps and multi-frame energy accumulation.Besides, contrastive learning is adopted to further improve the reliability of pseudo-labels, by calculating the similarity between positive and negative samples in feature subspace.Moreover, we propose a long-short term motion-aware learning scheme to simultaneously model the local motion patterns and global motion trajectory of small targets.The extensive experiments on two public datasets (DAUB and ITSDT-15K) verify that our weakly-supervised scheme could often outperform early fully-supervised methods. Even, its performance could reach over 90\% of state-of-the-art (SOTA) fully-supervised ones.
|
Different from general object detection, moving infrared small target detection faces huge challenges due to tiny target size and weak background contrast. Currently, most existing methods are fully-supervised, heavily relying on a large number of manual target-wise annotations.
|
https://arxiv.org/abs/2507.02454v1
|
https://arxiv.org/pdf/2507.02454v1.pdf
| null |
[
"Weiwei Duan",
"Luping Ji",
"Shengjia Chen",
"Sicheng Zhu",
"Jianghong Huang",
"Mao Ye"
] |
[
"Contrastive Learning",
"object-detection",
"Object Detection"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/virefsam-visual-reference-guided-segment
|
2507.02294
| null | null |
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
|
The Segment Anything Model (SAM), with its prompt-driven paradigm, exhibits strong generalization in generic segmentation tasks. However, applying SAM to remote sensing (RS) images still faces two major challenges. First, manually constructing precise prompts for each image (e.g., points or boxes) is labor-intensive and inefficient, especially in RS scenarios with dense small objects or spatially fragmented distributions. Second, SAM lacks domain adaptability, as it is pre-trained primarily on natural images and struggles to capture RS-specific semantics and spatial characteristics, especially when segmenting novel or unseen classes. To address these issues, inspired by few-shot learning, we propose ViRefSAM, a novel framework that guides SAM utilizing only a few annotated reference images that contain class-specific objects. Without requiring manual prompts, ViRefSAM enables automatic segmentation of class-consistent objects across RS images. Specifically, ViRefSAM introduces two key components while keeping SAM's original architecture intact: (1) a Visual Contextual Prompt Encoder that extracts class-specific semantic clues from reference images and generates object-aware prompts via contextual interaction with target images; and (2) a Dynamic Target Alignment Adapter, integrated into SAM's image encoder, which mitigates the domain gap by injecting class-specific semantics into target image features, enabling SAM to dynamically focus on task-relevant regions. Extensive experiments on three few-shot segmentation benchmarks, including iSAID-5$^i$, LoveDA-2$^i$, and COCO-20$^i$, demonstrate that ViRefSAM enables accurate and automatic segmentation of unseen classes by leveraging only a few reference images and consistently outperforms existing few-shot segmentation methods across diverse datasets.
| null |
https://arxiv.org/abs/2507.02294v1
|
https://arxiv.org/pdf/2507.02294v1.pdf
| null |
[
"Hanbo Bi",
"Yulong Xu",
"Ya Li",
"Yongqiang Mao",
"Boyuan Tong",
"Chongyang Li",
"Chunbo Lang",
"Wenhui Diao",
"Hongqi Wang",
"Yingchao Feng",
"Xian Sun"
] |
[
"Few-Shot Learning",
"Segmentation"
] | 2025-07-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Adapter",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Adapter",
"source_title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing",
"source_url": "https://arxiv.org/abs/2101.03289v5"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Segment Anything Model",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Segmentation Models",
"parent": null
},
"name": "SAM",
"source_title": "Segment Anything",
"source_url": "https://arxiv.org/abs/2304.02643v1"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/noctis-novel-object-cyclic-threshold-based
|
2507.01463
| null | null |
NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
|
Instance segmentation of novel objects instances in RGB images, given some example images for each object, is a well known problem in computer vision. Designing a model general enough to be employed, for all kinds of novel objects, without (re-) training, has proven to be a difficult task. To handle this, we propose a simple, yet powerful, framework, called: Novel Object Cyclic Threshold based Instance Segmentation (NOCTIS). This work stems from and improves upon previous ones like CNOS, SAM-6D and NIDS-Net; thus, it also leverages on recent vision foundation models, namely: Grounded-SAM 2 and DINOv2. It utilises Grounded-SAM 2 to obtain object proposals with precise bounding boxes and their corresponding segmentation masks; while DINOv2's zero-shot capabilities are employed to generate the image embeddings. The quality of those masks, together with their embeddings, is of vital importance to our approach; as the proposal-object matching is realized by determining an object matching score based on the similarity of the class embeddings and the average maximum similarity of the patch embeddings. Differently to SAM-6D, calculating the latter involves a prior patch filtering based on the distance between each patch and its corresponding cyclic/roundtrip patch in the image grid. Furthermore, the average confidence of the proposals' bounding box and mask is used as an additional weighting factor for the object matching score. We empirically show that NOCTIS, without further training/fine tuning, outperforms the best RGB and RGB-D methods on the seven core datasets of the BOP 2023 challenge for the "Model-based 2D segmentation of unseen objects" task.
|
The quality of those masks, together with their embeddings, is of vital importance to our approach; as the proposal-object matching is realized by determining an object matching score based on the similarity of the class embeddings and the average maximum similarity of the patch embeddings.
|
https://arxiv.org/abs/2507.01463v1
|
https://arxiv.org/pdf/2507.01463v1.pdf
| null |
[
"Max Gandyra",
"Alessandro Santonicola",
"Michael Beetz"
] |
[
"Instance Segmentation",
"Object",
"Segmentation",
"Semantic Segmentation"
] | 2025-07-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/autoadaptive-medical-segment-anything-model
|
2507.01828
| null | null |
Autoadaptive Medical Segment Anything Model
|
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automatic, and annotation-efficient methods of training these models. We propose ADA-SAM (automated, domain-specific, and adaptive segment anything model), a novel multitask learning framework for medical image segmentation that leverages class activation maps from an auxiliary classifier to guide the predictions of the semi-supervised segmentation branch, which is based on the Segment Anything (SAM) framework. Additionally, our ADA-SAM model employs a novel gradient feedback mechanism to create a learnable connection between the segmentation and classification branches by using the segmentation gradients to guide and improve the classification predictions. We validate ADA-SAM on real-world clinical data collected during rehabilitation trials, and demonstrate that our proposed method outperforms both fully-supervised and semi-supervised baselines by double digits in limited label settings. Our code is available at: https://github.com/tbwa233/ADA-SAM.
|
We propose ADA-SAM (automated, domain-specific, and adaptive segment anything model), a novel multitask learning framework for medical image segmentation that leverages class activation maps from an auxiliary classifier to guide the predictions of the semi-supervised segmentation branch, which is based on the Segment Anything (SAM) framework.
|
https://arxiv.org/abs/2507.01828v1
|
https://arxiv.org/pdf/2507.01828v1.pdf
| null |
[
"Tyler Ward",
"Meredith K. Owen",
"O'Kira Coleman",
"Brian Noehren",
"Abdullah-Al-Zubaer Imran"
] |
[
"Image Segmentation",
"Medical Image Segmentation",
"model",
"Segmentation",
"Semantic Segmentation"
] | 2025-07-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Auxiliary Classifiers** are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the end of the network. The motivation is to push useful gradients to the lower layers to make them immediately useful and improve the convergence during training by combatting the vanishing gradient problem. They are notably used in the Inception family of convolutional neural networks.",
"full_name": "Auxiliary Classifier",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "The following is a list of miscellaneous components used in neural networks.",
"name": "Miscellaneous Components",
"parent": null
},
"name": "Auxiliary Classifier",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/boosting-adversarial-transferability-against
|
2507.01791
| null | null |
Boosting Adversarial Transferability Against Defenses via Multi-Scale Transformation
|
The transferability of adversarial examples poses a significant security challenge for deep neural networks, which can be attacked without knowing anything about them. In this paper, we propose a new Segmented Gaussian Pyramid (SGP) attack method to enhance the transferability, particularly against defense models. Unlike existing methods that generally focus on single-scale images, our approach employs Gaussian filtering and three types of downsampling to construct a series of multi-scale examples. Then, the gradients of the loss function with respect to each scale are computed, and their average is used to determine the adversarial perturbations. The proposed SGP can be considered an input transformation with high extensibility that is easily integrated into most existing adversarial attacks. Extensive experiments demonstrate that in contrast to the state-of-the-art methods, SGP significantly enhances attack success rates against black-box defense models, with average attack success rates increasing by 2.3% to 32.6%, based only on transferability.
| null |
https://arxiv.org/abs/2507.01791v1
|
https://arxiv.org/pdf/2507.01791v1.pdf
| null |
[
"Zihong Guo",
"Chen Wan",
"Yayin Zheng",
"Hailing Kuang",
"Xiaohai Lu"
] |
[] | 2025-07-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/mamba-guided-boundary-prior-matters-a-new
|
2507.01509
| null | null |
Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation
|
Polyp segmentation in colonoscopy images is crucial for early detection and diagnosis of colorectal cancer. However, this task remains a significant challenge due to the substantial variations in polyp shape, size, and color, as well as the high similarity between polyps and surrounding tissues, often compounded by indistinct boundaries. While existing encoder-decoder CNN and transformer-based approaches have shown promising results, they struggle with stable segmentation performance on polyps with weak or blurry boundaries. These methods exhibit limited abilities to distinguish between polyps and non-polyps and capture essential boundary cues. Moreover, their generalizability still falls short of meeting the demands of real-time clinical applications. To address these limitations, we propose SAM-MaGuP, a groundbreaking approach for robust polyp segmentation. By incorporating a boundary distillation module and a 1D-2D Mamba adapter within the Segment Anything Model (SAM), SAM-MaGuP excels at resolving weak boundary challenges and amplifies feature learning through enriched global contextual interactions. Extensive evaluations across five diverse datasets reveal that SAM-MaGuP outperforms state-of-the-art methods, achieving unmatched segmentation accuracy and robustness. Our key innovations, a Mamba-guided boundary prior and a 1D-2D Mamba block, set a new benchmark in the field, pushing the boundaries of polyp segmentation to new heights.
|
Polyp segmentation in colonoscopy images is crucial for early detection and diagnosis of colorectal cancer.
|
https://arxiv.org/abs/2507.01509v1
|
https://arxiv.org/pdf/2507.01509v1.pdf
| null |
[
"Tapas K. Dutta",
"Snehashis Majhi",
"Deepak Ranjan Nayak",
"Debesh Jha"
] |
[
"Mamba",
"Segmentation"
] | 2025-07-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/state-spaces/mamba",
"description": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pre-training and downstream evaluation.",
"full_name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"introduced_year": 2000,
"main_collection": null,
"name": "Mamba",
"source_title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"source_url": "https://arxiv.org/abs/2312.00752v2"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Adapter",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Adapter",
"source_title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing",
"source_url": "https://arxiv.org/abs/2101.03289v5"
},
{
"code_snippet_url": null,
"description": "Dynamic Sparse Training method where weight mask is updated randomly periodically",
"full_name": "Sparse Evolutionary Training",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Sparsity",
"parent": null
},
"name": "SET",
"source_title": "Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science",
"source_url": "http://arxiv.org/abs/1707.04780v2"
}
] |
https://paperswithcode.com/paper/mask-aware-text-to-image-retrieval-referring
|
2506.22864
| null | null |
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval
|
Text-to-image retrieval (TIR) aims to find relevant images based on a textual query, but existing approaches are primarily based on whole-image captions and lack interpretability. Meanwhile, referring expression segmentation (RES) enables precise object localization based on natural language descriptions but is computationally expensive when applied across large image collections. To bridge this gap, we introduce Mask-aware TIR (MaTIR), a new task that unifies TIR and RES, requiring both efficient image search and accurate object segmentation. To address this task, we propose a two-stage framework, comprising a first stage for segmentation-aware image retrieval and a second stage for reranking and object grounding with a multimodal large language model (MLLM). We leverage SAM 2 to generate object masks and Alpha-CLIP to extract region-level embeddings offline at first, enabling effective and scalable online retrieval. Secondly, MLLM is used to refine retrieval rankings and generate bounding boxes, which are matched to segmentation masks. We evaluate our approach on COCO and D$^3$ datasets, demonstrating significant improvements in both retrieval accuracy and segmentation quality over previous methods.
| null |
https://arxiv.org/abs/2506.22864v1
|
https://arxiv.org/pdf/2506.22864v1.pdf
| null |
[
"Li-Cheng Shen",
"Jih-Kang Hsieh",
"Wei-Hua Li",
"Chu-Song Chen"
] |
[
"Cross-Modal Retrieval",
"Image Captioning",
"Image Retrieval",
"Large Language Model",
"Multimodal Large Language Model",
"Object",
"Object Localization",
"Referring Expression",
"Referring Expression Segmentation",
"Reranking",
"Retrieval",
"Segmentation",
"Semantic Segmentation"
] | 2025-06-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Segment Anything Model",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Segmentation Models",
"parent": null
},
"name": "SAM",
"source_title": "Segment Anything",
"source_url": "https://arxiv.org/abs/2304.02643v1"
}
] |
https://paperswithcode.com/paper/decoupled-seg-tokens-make-stronger-reasoning
|
2506.22880
| null | null |
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
|
Existing video segmenter and grounder approaches, exemplified by Sa2VA, directly fuse features within segmentation models. This often results in an undesirable entanglement of dynamic visual information and static semantics, thereby degrading segmentation accuracy. To systematically mitigate this issue, we propose DeSa2VA, a decoupling-enhanced prompting scheme integrating text pre-training and a linear decoupling module to address the information processing limitations inherent in SAM-2. Specifically, first, we devise a pre-training paradigm that converts textual ground-truth labels into point-level prompts while generating corresponding text masks. These masks are refined through a hybrid loss function to strengthen the model's semantic grounding capabilities. Next, we employ linear projection to disentangle hidden states that generated by a large language model into distinct textual and visual feature subspaces. Finally, a dynamic mask fusion strategy synergistically combines these decoupled features through triple supervision from predicted text/visual masks and ground-truth annotations. Extensive experiments demonstrate state-of-the-art performance across diverse tasks, including image segmentation, image question answering, video segmentation, and video question answering. Our codes are available at https://github.com/longmalongma/DeSa2VA.
|
Existing video segmenter and grounder approaches, exemplified by Sa2VA, directly fuse features within segmentation models.
|
https://arxiv.org/abs/2506.22880v1
|
https://arxiv.org/pdf/2506.22880v1.pdf
| null |
[
"Dang Jisheng",
"Wu Xudong",
"Wang Bimei",
"Lv Ning",
"Chen Jiayu",
"Jingwen Zhao",
"Yichu liu",
"Jizhao Liu",
"Juncheng Li",
"Teng Wang"
] |
[
"Image Segmentation",
"Large Language Model",
"Question Answering",
"Segmentation",
"Semantic Segmentation",
"Video Question Answering",
"Video Segmentation",
"Video Semantic Segmentation",
"Visual Question Answering (VQA)"
] | 2025-06-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/groundingdino-us-sam-text-prompted-multi
|
2506.23903
| null | null |
GroundingDINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models
|
Accurate and generalizable object segmentation in ultrasound imaging remains a significant challenge due to anatomical variability, diverse imaging protocols, and limited annotated data. In this study, we propose a prompt-driven vision-language model (VLM) that integrates Grounding DINO with SAM2 to enable object segmentation across multiple ultrasound organs. A total of 18 public ultrasound datasets, encompassing the breast, thyroid, liver, prostate, kidney, and paraspinal muscle, were utilized. These datasets were divided into 15 for fine-tuning and validation of Grounding DINO using Low Rank Adaptation (LoRA) to the ultrasound domain, and 3 were held out entirely for testing to evaluate performance in unseen distributions. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art segmentation methods, including UniverSeg, MedSAM, MedCLIP-SAM, BiomedParse, and SAMUS on most seen datasets while maintaining strong performance on unseen datasets without additional fine-tuning. These results underscore the promise of VLMs in scalable and robust ultrasound image analysis, reducing dependence on large, organ-specific annotated datasets. We will publish our code on code.sonography.ai after acceptance.
| null |
https://arxiv.org/abs/2506.23903v1
|
https://arxiv.org/pdf/2506.23903v1.pdf
| null |
[
"Hamza Rasaee",
"Taha Koleilat",
"Hassan Rivaz"
] |
[
"Organ Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2025-06-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google-research/vision_transformer",
"description": "The **Vision Transformer**, or **ViT**, is a model for image classification that employs a [Transformer](https://paperswithcode.com/method/transformer)-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard [Transformer](https://paperswithcode.com/method/transformer) encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.",
"full_name": "Vision Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Vision Transformer",
"source_title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
"source_url": "https://arxiv.org/abs/2010.11929v2"
},
{
"code_snippet_url": "https://github.com/facebookresearch/dino/blob/main/main_dino.py",
"description": "**DINO** (self-distillation with no labels) is a self-supervised learning method that directly predicts the output of a teacher network - built with a momentum encoder - using a standard cross-entropy loss. \r\n\r\nIn the example to the right, DINO is illustrated in the case of one single pair of views $\\left(x\\_{1}, x\\_{2}\\right)$ for simplicity.\r\nThe model passes two different random transformations of an input image to the student and teacher networks. Both networks have the same architecture but other parameters.\r\nThe output of the teacher network is centered with a mean computed over the batch. Each network outputs a $K$ dimensional feature normalized with a temperature [softmax](https://paperswithcode.com/method/softmax) over the feature dimension.\r\nTheir similarity is then measured with a cross-entropy loss.\r\nA stop-gradient (sg) operator is applied to the teacher to propagate gradients only through the student.\r\nThe teacher parameters are updated with the student parameters' exponential moving average (ema).",
"full_name": "self-DIstillation with NO labels",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Self-Supervised Learning** refers to a category of methods where we learn representations in a self-supervised way (i.e without labels). These methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. Below you can find a continuously updating list of self-supervised methods.",
"name": "Self-Supervised Learning",
"parent": null
},
"name": "DINO",
"source_title": "Emerging Properties in Self-Supervised Vision Transformers",
"source_url": "https://arxiv.org/abs/2104.14294v2"
}
] |
https://paperswithcode.com/paper/votesplat-hough-voting-gaussian-splatting-for
|
2506.22799
| null | null |
VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding
|
3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometric and appearance modeling, lacking deeper scene understanding while also incurring high training costs that complicate the originally streamlined differentiable rendering pipeline. To this end, we propose VoteSplat, a novel 3D scene understanding framework that integrates Hough voting with 3DGS. Specifically, Segment Anything Model (SAM) is utilized for instance segmentation, extracting objects, and generating 2D vote maps. We then embed spatial offset vectors into Gaussian primitives. These offsets construct 3D spatial votes by associating them with 2D image votes, while depth distortion constraints refine localization along the depth axis. For open-vocabulary object localization, VoteSplat maps 2D image semantics to 3D point clouds via voting points, reducing training costs associated with high-dimensional CLIP features while preserving semantic unambiguity. Extensive experiments demonstrate effectiveness of VoteSplat in open-vocabulary 3D instance localization, 3D point cloud understanding, click-based 3D object localization, hierarchical segmentation, and ablation studies. Our code is available at https://sy-ja.github.io/votesplat/
| null |
https://arxiv.org/abs/2506.22799v1
|
https://arxiv.org/pdf/2506.22799v1.pdf
| null |
[
"Minchao Jiang",
"Shunyu Jia",
"Jiaming Gu",
"Xiaoyuan Lu",
"Guangming Zhu",
"Anqi Dong",
"Liang Zhang"
] |
[
"3DGS",
"Instance Segmentation",
"Novel View Synthesis",
"Object Localization",
"Scene Understanding",
"Semantic Segmentation"
] | 2025-06-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/OpenAI/CLIP",
"description": "**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. \r\n\r\nFor pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. \r\n\r\nImage credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)",
"full_name": "Contrastive Language-Image Pre-training",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Representations",
"parent": null
},
"name": "CLIP",
"source_title": "Learning Transferable Visual Models From Natural Language Supervision",
"source_url": "https://arxiv.org/abs/2103.00020v1"
},
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
https://paperswithcode.com/paper/foundation-models-for-zero-shot-segmentation
|
2506.24039
| null | null |
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data
|
Zero-shot and prompt-based technologies capitalized on using frequently occurring images to transform visual reasoning tasks, which explains why such technologies struggle with valuable yet scarce scientific image sets. In this work, we propose Zenesis, a comprehensive no-code interactive platform designed to minimize barriers posed by data readiness for scientific images. We develop lightweight multi-modal adaptation techniques that enable zero-shot operation on raw scientific data, along with human-in-the-loop refinement and heuristic-based temporal enhancement options. We demonstrate the performance of our approach through comprehensive comparison and validation on challenging Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) data of catalyst-loaded membranes. Zenesis significantly outperforms baseline methods, achieving an average accuracy of 0.947, an Intersection over Union (IOU) of 0.858, and a Dice score of 0.923 for amorphous catalyst samples and accuracy of 0.987, an IOU of 0.857, and a Dice score of 0.923 for crystalline samples. These results mark a substantial improvement over traditional methods like Otsu thresholding and even advanced models like Segment Anything Model (SAM) when used in isolation. Our results demonstrate that Zenesis is a powerful tool for scientific applications, particularly in fields where high-quality annotated datasets are unavailable, accelerating accurate analysis of experimental imaging.
| null |
https://arxiv.org/abs/2506.24039v1
|
https://arxiv.org/pdf/2506.24039v1.pdf
| null |
[
"Shubhabrata Mukherjee",
"Jack Lang",
"Obeen Kwon",
"Iryna Zenyuk",
"Valerie Brogden",
"Adam Weber",
"Daniela Ushizima"
] |
[
"Visual Reasoning",
"Zero Shot Segmentation"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/medsam-ca-a-cnn-augmented-vit-with-attention
|
2506.23700
| null | null |
MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation
|
Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning, where accurate boundary delineation is essential for precise lesion localization, organ identification, and quantitative assessment. In recent years, deep learning-based methods have significantly advanced segmentation accuracy. However, two major challenges remain. First, the performance of these methods heavily relies on large-scale annotated datasets, which are often difficult to obtain in medical scenarios due to privacy concerns and high annotation costs. Second, clinically challenging scenarios, such as low contrast in certain imaging modalities and blurry lesion boundaries caused by malignancy, still pose obstacles to precise segmentation. To address these challenges, we propose MedSAM-CA, an architecture-level fine-tuning approach that mitigates reliance on extensive manual annotations by adapting the pretrained foundation model, Medical Segment Anything (MedSAM). MedSAM-CA introduces two key components: the Convolutional Attention-Enhanced Boundary Refinement Network (CBR-Net) and the Attention-Enhanced Feature Fusion Block (Atte-FFB). CBR-Net operates in parallel with the MedSAM encoder to recover boundary information potentially overlooked by long-range attention mechanisms, leveraging hierarchical convolutional processing. Atte-FFB, embedded in the MedSAM decoder, fuses multi-level fine-grained features from skip connections in CBR-Net with global representations upsampled within the decoder to enhance boundary delineation accuracy. Experiments on publicly available datasets covering dermoscopy, CT, and MRI imaging modalities validate the effectiveness of MedSAM-CA. On dermoscopy dataset, MedSAM-CA achieves 94.43% Dice with only 2% of full training data, reaching 97.25% of full-data training performance, demonstrating strong effectiveness in low-resource clinical settings.
| null |
https://arxiv.org/abs/2506.23700v1
|
https://arxiv.org/pdf/2506.23700v1.pdf
| null |
[
"Peiting Tian",
"Xi Chen",
"Haixia Bi",
"Fan Li"
] |
[
"Decoder",
"Image Segmentation",
"Medical Image Segmentation",
"Semantic Segmentation"
] | 2025-06-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/diffusion-model-based-data-augmentation
|
2506.23664
| null | null |
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
|
Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-intensive manual image annotation by clinical experts. To overcome these challenges, synthetic medical data generation offers a promising solution. Generative AI (GenAI), employing generative deep learning models, has proven effective at producing realistic synthetic images. This study proposes a novel mask-guided GenAI approach using diffusion models to generate synthetic fetal head ultrasound images paired with segmentation masks. These synthetic pairs augment real datasets for supervised fine-tuning of the Segment Anything Model (SAM). Our results show that the synthetic data captures real image features effectively, and this approach reaches state-of-the-art fetal head segmentation, especially when trained with a limited number of real image-mask pairs. In particular, the segmentation reaches Dice Scores of 94.66\% and 94.38\% using a handful of ultrasound images from the Spanish and African cohorts, respectively. Our code, models, and data are available on GitHub.
| null |
https://arxiv.org/abs/2506.23664v2
|
https://arxiv.org/pdf/2506.23664v2.pdf
| null |
[
"Fangyijie Wang",
"Kevin Whelan",
"Félix Balado",
"Kathleen M. Curran",
"Guénolé Silvestre"
] |
[
"Data Augmentation",
"Segmentation"
] | 2025-06-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Image Generation Models",
"parent": null
},
"name": "Diffusion",
"source_title": "Denoising Diffusion Probabilistic Models",
"source_url": "https://arxiv.org/abs/2006.11239v2"
}
] |
https://paperswithcode.com/paper/surgtpgs-semantic-3d-surgical-scene
|
2506.23309
| null | null |
SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
|
In contemporary surgical research and practice, accurately comprehending 3D surgical scenes with text-promptable capabilities is particularly crucial for surgical planning and real-time intra-operative guidance, where precisely identifying and interacting with surgical tools and anatomical structures is paramount. However, existing works focus on surgical vision-language model (VLM), 3D reconstruction, and segmentation separately, lacking support for real-time text-promptable 3D queries. In this paper, we present SurgTPGS, a novel text-promptable Gaussian Splatting method to fill this gap. We introduce a 3D semantics feature learning strategy incorporating the Segment Anything model and state-of-the-art vision-language models. We extract the segmented language features for 3D surgical scene reconstruction, enabling a more in-depth understanding of the complex surgical environment. We also propose semantic-aware deformation tracking to capture the seamless deformation of semantic features, providing a more precise reconstruction for both texture and semantic features. Furthermore, we present semantic region-aware optimization, which utilizes regional-based semantic information to supervise the training, particularly promoting the reconstruction quality and semantic smoothness. We conduct comprehensive experiments on two real-world surgical datasets to demonstrate the superiority of SurgTPGS over state-of-the-art methods, highlighting its potential to revolutionize surgical practices. SurgTPGS paves the way for developing next-generation intelligent surgical systems by enhancing surgical precision and safety. Our code is available at: https://github.com/lastbasket/SurgTPGS.
|
We also propose semantic-aware deformation tracking to capture the seamless deformation of semantic features, providing a more precise reconstruction for both texture and semantic features.
|
https://arxiv.org/abs/2506.23309v2
|
https://arxiv.org/pdf/2506.23309v2.pdf
| null |
[
"Yiming Huang",
"Long Bai",
"Beilei Cui",
"Kun Yuan",
"Guankun Wang",
"Mobarak I. Hoque",
"Nicolas Padoy",
"Nassir Navab",
"Hongliang Ren"
] |
[
"3D Reconstruction",
"Scene Understanding"
] | 2025-06-29T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Focus",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Focus",
"source_title": "Focus Your Attention (with Adaptive IIR Filters)",
"source_url": "https://arxiv.org/abs/2305.14952v2"
}
] |
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