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900 | Generating Illustrated Instructions | http://arxiv.org/abs/2312.04552 | Sachit Menon, Ishan Misra, Rohit Girdhar | 2,312.04552 | We introduce a new task of generating "Illustrated Instructions" i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task and formalize it through a suite of automatic and human evaluation metrics designed to measure the validity consistency and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases users even prefer it to human-generated articles. Most notably it enables various new and exciting applications far beyond what static articles on the web can provide such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation. | [] | [] | [] | [] | 900 |
901 | Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation | Guangrui Li | null | This paper tackles the domain adaptation problem in point cloud semantic segmentation which performs adaptation from a fully labeled domain (source domain) to an unlabeled target domain. Due to the unordered property of point clouds LiDAR scans typically show varying geometric structures across different regions in terms of density noises etc hence leading to increased dynamics on context. However such characteristics are not consistent across domains due to the difference in sensors environments etc thus hampering the effective scene comprehension across domains. To solve this we propose Cooperative Context Learning that performs context modeling and modulation from different aspects but in a cooperative manner. Specifically we first devise context embeddings to discover and model contextual relationships with close neighbors in a learnable manner. Then with the context embeddings from two domains we introduce a set of learnable prototypes to attend and associate them under the attention paradigm. As a result these prototypes naturally establish long-range dependency across regions and domains thereby encouraging the transfer of context knowledge and easing the adaptation. Moreover the attention in turn attunes and guides the local context modeling and urges them to focus on the domain-invariant context knowledge thus promoting the adaptation in a cooperative manner. Experiments on representative benchmarks verify that our method attains the new state-of-the-art. | [] | [] | [] | [] | 901 |
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902 | Robust Image Denoising through Adversarial Frequency Mixup | Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, Bohyung Han | null | Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks which are trained using a limited spectrum of real noise distributions and thus show poor robustness to out-of-distribution real noise types. To alleviate this issue we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics all the while preserving the properties of authentic real-world noise. Subsequently incorporating these noisy images into the training pipeline enhances the denoising network's robustness to variations in noise distributions. Extensive experiments and analyses conducted on a wide range of real noise benchmarks demonstrate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions. The code is available at https://github.com/dhryougit/AFM. | [] | [] | [] | [] | 902 |
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903 | HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions | http://arxiv.org/abs/2403.18575 | Hao Xu, Haipeng Li, Yinqiao Wang, Shuaicheng Liu, Chi-Wing Fu | 2,403.18575 | Reconstructing 3D hand mesh robustly from a single image is very challenging due to the lack of diversity in existing real-world datasets. While data synthesis helps relieve the issue the syn-to-real gap still hinders its usage. In this work we present HandBooster a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples. First we construct versatile content-aware conditions to guide a diffusion model to produce realistic images with diverse hand appearances poses views and backgrounds; favorably accurate 3D annotations are obtained for free. Then we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from the training set. Equipped with our method several baselines can be significantly improved beyond the SOTA on the HO3D and DexYCB benchmarks. Our code will be released on https://github.com/hxwork/HandBooster_Pytorch. | [] | [] | [] | [] | 903 |
904 | A-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection | Hanshi Wang, Zhipeng Zhang, Jin Gao, Weiming Hu | null | This work proposes the first online asymmetric semi-supervised framework namely A-Teacher for LiDAR-based 3D object detection. Our motivation stems from the observation that 1) existing symmetric teacher-student methods for semi-supervised 3D object detection have characterized simplicity but impede the distillation performance between teacher and student because of the demand for an identical model structure and input data format. 2) The offline asymmetric methods with a complex teacher model constructed differently can generate more precise pseudo-labels but is challenging to jointly optimize the teacher and student model. Consequently in this paper we devise a different path from the conventional paradigm which can harness the capacity of a strong teacher while preserving the advantages of online teacher model updates. The essence is the proposed attention-based refinement model that can be seamlessly integrated into a vanilla teacher. The refinement model works in the divide-and-conquer manner that respectively handles three challenging scenarios including 1) objects detected in the current timestamp but with suboptimal box quality 2) objects are missed in the current timestamp but are detected in past or future frames 3) objects are neglected in all frames. It is worth noting that even while tackling these complex cases our model retains the efficiency of the online teacher-student semi-supervised framework. Experimental results on Waymo show that our method outperforms previous state-of-the-art HSSDA for 4.7 on mAP (L1) while consuming fewer training resources. | [] | [] | [] | [] | 904 |
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905 | GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo | http://arxiv.org/abs/2404.07992 | Jiang Wu, Rui Li, Haofei Xu, Wenxun Zhao, Yu Zhu, Jinqiu Sun, Yanning Zhang | 2,404.07992 | Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However directly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related methods either seek selective aggregation or improve aggregated depth in the 2D space both are unable to handle geometric inconsistency in the cost volume effectively. In this paper we propose GoMVS to aggregate geometrically consistent costs yielding better utilization of adjacent geometries. More specifically we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals then uses the correspondence to propagate adjacent costs to the reference geometry followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU Tanks & Temple and ETH3D datasets. Notably our method ranks 1st on the Tanks & Temple Advanced benchmark. Code is available at https://github.com/Wuuu3511/GoMVS. | [] | [] | [] | [] | 905 |
906 | Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods | Mengyu Dai, Amir Hossein Raffiee, Aashish Jain, Joshua Correa | null | Retrieval tasks play central roles in real-world machine learning systems such as search engines recommender systems and retrieval-augmented generation (RAG). Achieving decent performance in these tasks often requires fine-tuning various pre-trained models on specific datasets and selecting the best candidate a process that can be both time and resource-consuming. To tackle the problem we introduce a novel and efficient method called RetMMD that leverages Maximum Mean Discrepancy (MMD) and kernel methods to assess the transferability of pretrained models in retrieval tasks. RetMMD is calculated on pretrained model and target dataset without any fine-tuning involved. Specifically given some query we quantify the distribution discrepancy between relevant and irrelevant document embeddings by estimating the similarities within their mappings in the fine-tuned embedding space through kernel method. This discrepancy is averaged over multiple queries taking into account the distribution characteristics of the target dataset. Experiments suggest that the proposed metric calculated on pre-trained models closely aligns with retrieval performance post-fine-tuning. The observation holds across a variety of datasets including image text and multi-modal domains indicating the potential of using MMD and kernel methods for transfer learning evaluation in retrieval scenarios. In addition we also design a way of evaluating dataset transferability for retrieval tasks with experimental results demonstrating the effectiveness of the proposed approach. | [] | [] | [] | [] | 906 |
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907 | AnyScene: Customized Image Synthesis with Composited Foreground | Ruidong Chen, Lanjun Wang, Weizhi Nie, Yongdong Zhang, An-An Liu | null | Recent advancements in text-to-image technology have significantly advanced the field of image customization. Among various applications the task of customizing diverse scenes for user-specified composited elements holds great application value but has not been extensively explored. Addressing this gap we propose AnyScene a specialized framework designed to create varied scenes from composited foreground using textual prompts. AnyScene addresses the primary challenges inherent in existing methods particularly scene disharmony due to a lack of foreground semantic understanding and distortion of foreground elements. Specifically we develop a foreground injection module that guides a pre-trained diffusion model to generate cohesive scenes in visual harmony with the provided foreground. To enhance robust generation we implement a layout control strategy that prevents distortions of foreground elements. Furthermore an efficient image blending mechanism seamlessly reintegrates foreground details into the generated scenes producing outputs with overall visual harmony and precise foreground details. In addition we propose a new benchmark and a series of quantitative metrics to evaluate this proposed image customization task. Extensive experimental results demonstrate the effectiveness of AnyScene which confirms its potential in various applications. | [] | [] | [] | [] | 907 |
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908 | Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts | http://arxiv.org/abs/2402.19215 | Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan | 2,402.19215 | Super-resolution (SR) is an ill-posed inverse problem where the size of the set of feasible solutions that are consistent with a given low-resolution image is very large. Many algorithms have been proposed to find a "good" solution among the feasible solutions that strike a balance between fidelity and perceptual quality. Unfortunately all known methods generate artifacts and hallucinations while trying to reconstruct high-frequency (HF) image details. A fundamental question is: Can a model learn to distinguish genuine image details from artifacts? Although some recent works focused on the differentiation of details and artifacts this is a very challenging problem and a satisfactory solution is yet to be found. This paper shows that the characterization of genuine HF details versus artifacts can be better learned by training GAN-based SR models using wavelet-domain loss functions compared to RGB-domain or Fourier-space losses. Although wavelet-domain losses have been used in the literature before they have not been used in the context of the SR task. More specifically we train the discriminator only on the HF wavelet sub-bands instead of on RGB images and the generator is trained by a fidelity loss over wavelet subbands to make it sensitive to the scale and orientation of structures. Extensive experimental results demonstrate that our model achieves better perception-distortion trade-off according to multiple objective measures and visual evaluations. | [] | [] | [] | [] | 908 |
909 | Visual Objectification in Films: Towards a New AI Task for Video Interpretation | http://arxiv.org/abs/2401.13296 | Julie Tores, Lucile Sassatelli, Hui-Yin Wu, Clement Bergman, Léa Andolfi, Victor Ecrement, Frédéric Precioso, Thierry Devars, Magali Guaresi, Virginie Julliard, Sarah Lecossais | 2,401.13296 | In film gender studies the concept of "male gaze" refers to the way the characters are portrayed on-screen as objects of desire rather than subjects. In this article we introduce a novel video-interpretation task to detect character objectification in films. The purpose is to reveal and quantify the usage of complex temporal patterns operated in cinema to produce the cognitive perception of objectification. We introduce the ObyGaze12 dataset made of 1914 movie clips densely annotated by experts for objectification concepts identified in film studies and psychology. We evaluate recent vision models show the feasibility of the task and where the challenges remain with concept bottleneck models. Our new dataset and code are made available to the community. | [] | [] | [] | [] | 909 |
910 | OMG-Seg: Is One Model Good Enough For All Segmentation? | Xiangtai Li, Haobo Yuan, Wei Li, Henghui Ding, Size Wu, Wenwei Zhang, Yining Li, Kai Chen, Chen Change Loy | null | In this work we address various segmentation tasks each traditionally tackled by distinct or partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently and effectively handle all the segmentation tasks including image semantic instance and panoptic segmentation as well as their video counterparts open vocabulary settings prompt-driven interactive segmentation like SAM and video object segmentation. To our knowledge this is the first model to handle all these tasks in one model and achieve satisfactory performance. We show that OMG-Seg a transformer-based encoder-decoder architecture with task-specific queries and outputs can support over ten distinct segmentation tasks and yet significantly reduce computational and parameter overhead across various tasks and datasets. We rigorously evaluate the inter-task influences and correlations during co-training. Code and models are available at https://github.com/lxtGH/OMG-Seg. | [] | [] | [] | [] | 910 |
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911 | BiTT: Bi-directional Texture Reconstruction of Interacting Two Hands from a Single Image | http://arxiv.org/abs/2403.08262 | Minje Kim, Tae-Kyun Kim | 2,403.08262 | Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes some recent work has tackled the reconstruction of hand textures on top of shapes. However these methods are often limited to capturing pixels on the visible side of a hand requiring diverse views of the hand in a video or multiple images as input. In this paper we propose a novel method BiTT(Bi-directional Texture reconstruction of Two hands) which is the first end-to-end train- able method for relightable pose-free texture reconstruction of two interacting hands taking only a single RGB image by three novel components: 1) bi-directional (left ? right) texture reconstruction using the texture symmetry of left / right hands 2) utilizing a texture parametric model for hand texture recovery and 3) the overall coarse-to-fine stage pipeline for reconstructing personalized texture of two interacting hands. BiTT first estimates the scene light condition and albedo image from an input image then reconstructs the texture of both hands through the texture parametric model and bi-directional texture reconstructor. In experiments using InterHand2.6M and RGB2Hands datasets our method significantly outperforms state-of-the-art hand texture reconstruction methods quantitatively and qualitatively. The code is available at https://github.com/ yunminjin2/BiTT. | [] | [] | [] | [] | 911 |
912 | DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection | http://arxiv.org/abs/2404.09216 | Lewei Yao, Renjie Pi, Jianhua Han, Xiaodan Liang, Hang Xu, Wei Zhang, Zhenguo Li, Dan Xu | 2,404.09216 | Existing open-vocabulary object detectors typically require a predefined set of categories from users significantly confining their application scenarios. In this paper we introduce DetCLIPv3 a high-performing detector that excels not only at both open-vocabulary object detection but also generating hierarchical labels for detected objects. DetCLIPv3 is characterized by three core designs: 1. Versatile model architecture: we derive a robust open-set detection framework which is further empowered with generation ability via the integration of a caption head. 2. High information density data: we develop an auto-annotation pipeline leveraging visual large language model to refine captions for large-scale image-text pairs providing rich multi-granular object labels to enhance the training. 3. Efficient training strategy: we employ a pre-training stage with low-resolution inputs that enables the object captioner to efficiently learn a broad spectrum of visual concepts from extensive image-text paired data. This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance. With these effective designs DetCLIPv3 demonstrates superior open-vocabulary detection performance e.g. our Swin-T backbone model achieves a notable 47.0 zero-shot fixed AP on the LVIS minival benchmark outperforming GLIPv2 GroundingDINO and DetCLIPv2 by 18.0/19.6/6.6 AP respectively. DetCLIPv3 also achieves a state-of-the-art 19.7 AP in dense captioning task on VG dataset showcasing its strong generative capability. | [] | [] | [] | [] | 912 |
913 | UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement | http://arxiv.org/abs/2404.14542 | Yaofeng Xie, Lingwei Kong, Kai Chen, Ziqiang Zheng, Xiao Yu, Zhibin Yu, Bing Zheng | 2,404.14542 | Learning-based underwater image enhancement (UIE) methods have made great progress. However the lack of large-scale and high-quality paired training samples has become the main bottleneck hindering the development of UIE. The inter-frame information in underwater videos can accelerate or optimize the UIE process. Thus we constructed the first large-scale high-resolution underwater video enhancement benchmark (UVEB) to promote the development of underwater vision.It contains 1308 pairs of video sequences and more than 453000 high-resolution with 38% Ultra-High-Definition (UHD) 4K frame pairs. UVEB comes from multiple countries containing various scenes and video degradation types to adapt to diverse and complex underwater environments. We also propose the first supervised underwater video enhancement method UVE-Net. UVE-Net converts the current frame information into convolutional kernels and passes them to adjacent frames for efficient inter-frame information exchange. By fully utilizing the redundant degraded information of underwater videos UVE-Net completes video enhancement better. Experiments show the effective network design and good performance of UVE-Net. | [] | [] | [] | [] | 913 |
914 | Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs | http://arxiv.org/abs/2404.07449 | Kanchana Ranasinghe, Satya Narayan Shukla, Omid Poursaeed, Michael S. Ryoo, Tsung-Yu Lin | 2,404.07449 | Integration of Large Language Models (LLMs) into visual domain tasks resulting in visual-LLMs (V-LLMs) has enabled exceptional performance in vision-language tasks particularly for visual question answering (VQA). However existing V-LLMs (e.g. BLIP-2 LLaVA) demonstrate weak spatial reasoning and localization awareness. Despite generating highly descriptive and elaborate textual answers these models fail at simple tasks like distinguishing a left vs right location. In this work we explore how image-space coordinate based instruction fine-tuning objectives could inject spatial awareness into V-LLMs. We discover optimal coordinate representations data-efficient instruction fine-tuning objectives and pseudo-data generation strategies that lead to improved spatial awareness in V-LLMs. Additionally our resulting model improves VQA across image and video domains reduces undesired hallucination and generates better contextual object descriptions. Experiments across 5 vision-language tasks involving 14 different datasets establish the clear performance improvements achieved by our proposed framework. | [] | [] | [] | [] | 914 |
915 | Monocular Identity-Conditioned Facial Reflectance Reconstruction | http://arxiv.org/abs/2404.00301 | Xingyu Ren, Jiankang Deng, Yuhao Cheng, Jia Guo, Chao Ma, Yichao Yan, Wenhan Zhu, Xiaokang Yang | 2,404.00301 | Recent 3D face reconstruction methods have made remarkable advancements yet there remain huge challenges in monocular high-quality facial reflectance reconstruction. Existing methods rely on a large amount of light-stage captured data to learn facial reflectance models. However the lack of subject diversity poses challenges in achieving good generalization and widespread applicability. In this paper we learn the reflectance prior in image space rather than UV space and present a framework named ID2Reflectance. Our framework can directly estimate the reflectance maps of a single image while using limited reflectance data for training. Our key insight is that reflectance data shares facial structures with RGB faces which enables obtaining expressive facial prior from inexpensive RGB data thus reducing the dependency on reflectance data. We first learn a high-quality prior for facial reflectance. Specifically we pretrain multi-domain facial feature codebooks and design a codebook fusion method to align the reflectance and RGB domains. Then we propose an identity-conditioned swapping module that injects facial identity from the target image into the pre-trained auto-encoder to modify the identity of the source reflectance image. Finally we stitch multi-view swapped reflectance images to obtain renderable assets. Extensive experiments demonstrate that our method exhibits excellent generalization capability and achieves state-of-the-art facial reflectance reconstruction results for in-the-wild faces. | [] | [] | [] | [] | 915 |
916 | C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video | http://arxiv.org/abs/2312.02753 | Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont | 2,312.02753 | Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive architectures with a high decoding complexity. Here we introduce C3 a neural compression method with strong rate-distortion (RD) performance that instead overfits a small model to each image or video separately. The resulting decoding complexity of C3 can be an order of magnitude lower than neural baselines with similar RD performance. C3 builds on COOL-CHIC [Ladune et al 2023] and makes several simple and effective improvements for images. We further develop new methodology to apply C3 to videos. On the CLIC2020 image benchmark we match the RD performance of VTM the reference implementation of the H.266 codec with less than3k MACs/pixel for decoding. On the UVG video benchmark we match the RD performance of the Video Compression Transformer [Mentzer er al 2022] a well-established neural video codec with less than 5k MACs/pixel for decoding. | [] | [] | [] | [] | 916 |
917 | Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors | http://arxiv.org/abs/2306.12041 | Nicolae-C?t?lin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah | 2,306.12041 | We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. The novelty of the proposed model is threefold. First we introduce an approach to weight tokens based on motion gradients thus shifting the focus from the static background scene to the foreground objects. Second we integrate a teacher decoder and a student decoder into our architecture leveraging the discrepancy between the outputs given by the two decoders to improve anomaly detection. Third we generate synthetic abnormal events to augment the training videos and task the masked AE model to jointly reconstruct the original frames (without anomalies) and the corresponding pixel-level anomaly maps. Our design leads to an efficient and effective model as demonstrated by the extensive experiments carried out on four benchmarks: Avenue ShanghaiTech UBnormal and UCSD Ped2. The empirical results show that our model achieves an excellent trade-off between speed and accuracy obtaining competitive AUC scores while processing 1655 FPS. Hence our model is between 8 and 70 times faster than competing methods. We also conduct an ablation study to justify our design. Our code is freely available at: https://github.com/ristea/aed-mae. | [] | [] | [] | [] | 917 |
918 | Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis | Zanlin Ni, Yulin Wang, Renping Zhou, Jiayi Guo, Jinyi Hu, Zhiyuan Liu, Shiji Song, Yuan Yao, Gao Huang | null | The field of image synthesis is currently flourishing due to the advancements in diffusion models. While diffusion models have been successful their computational intensity has prompted the pursuit of more efficient alternatives. As a representative work non-autoregressive Transformers (NATs) have been recognized for their rapid generation. However a major drawback of these models is their inferior performance compared to diffusion models. In this paper we aim to re-evaluate the full potential of NATs by revisiting the design of their training and inference strategies. Specifically we identify the complexities in properly configuring these strategies and indicate the possible sub-optimality in existing heuristic-driven designs. Recognizing this we propose to go beyond existing methods by directly solving the optimal strategies in an automatic framework. The resulting method named AutoNAT advances the performance boundaries of NATs notably and is able to perform comparably with the latest diffusion models with a significantly reduced inference cost. The effectiveness of AutoNAT is comprehensively validated on four benchmark datasets i.e. ImageNet-256 & 512 MS-COCO and CC3M. Code and pre-trained models will be available at https://github.com/LeapLabTHU/ImprovedNAT. | [] | [] | [] | [] | 918 |
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919 | Distilling Vision-Language Models on Millions of Videos | http://arxiv.org/abs/2401.06129 | Yue Zhao, Long Zhao, Xingyi Zhou, Jialin Wu, Chun-Te Chu, Hui Miao, Florian Schroff, Hartwig Adam, Ting Liu, Boqing Gong, Philipp Krahenbuhl, Liangzhe Yuan | 2,401.06129 | The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models but there simply is not enough human-curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video model by video-instruction-tuning (VIIT) is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance it surpasses the best prior result on open-ended NExT-QA by2.8%. Besides our model generates detailed descriptions for previously unseen videos which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%. As a side product we generate the largest video caption dataset to date. | [] | [] | [] | [] | 919 |
920 | ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D Image | Marco Pesavento, Yuanlu Xu, Nikolaos Sarafianos, Robert Maier, Ziyan Wang, Chun-Han Yao, Marco Volino, Edmond Boyer, Adrian Hilton, Tony Tung | null | Recent progress in human shape learning shows that neural implicit models are effective in generating 3D human surfaces from limited number of views and even from a single RGB image. However existing monocular approaches still struggle to recover fine geometric details such as face hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy which improves the accuracy of the signed distance field estimation of points that lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB surface normals point cloud or RGB-D data as input. In addition we introduce ANIM-Real a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera and our protocol to fine-tune ANIM enabling high-quality reconstruction from real-world human capture. | [] | [] | [] | [] | 920 |
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921 | Real-Time Simulated Avatar from Head-Mounted Sensors | http://arxiv.org/abs/2403.06862 | Zhengyi Luo, Jinkun Cao, Rawal Khirodkar, Alexander Winkler, Kris Kitani, Weipeng Xu | 2,403.06862 | We present SimXR a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets. Due to the challenging viewpoint of head-mounted cameras the human body is often clipped out of view making traditional image-based egocentric pose estimation challenging. On the other hand headset poses provide valuable information about overall body motion but lack fine-grained details about the hands and feet. To synergize headset poses with cameras we control a humanoid to track headset movement while analyzing input images to decide body movement. When body parts are seen the movements of hands and feet will be guided by the images; when unseen the laws of physics guide the controller to generate plausible motion. We design an end-to-end method that does not rely on any intermediate representations and learns to directly map from images and headset poses to humanoid control signals. To train our method we also propose a large-scale synthetic dataset created using camera configurations compatible with a commercially available VR headset (Quest 2) and show promising results on real-world captures. To demonstrate the applicability of our framework we also test it on an AR headset with a forward-facing camera. | [] | [] | [] | [] | 921 |
922 | Discovering Syntactic Interaction Clues for Human-Object Interaction Detection | Jinguo Luo, Weihong Ren, Weibo Jiang, Xi'ai Chen, Qiang Wang, Zhi Han, Honghai Liu | null | Recently Vision-Language Model (VLM) has greatly advanced the Human-Object Interaction (HOI) detection. The existing VLM-based HOI detectors typically adopt a hand-crafted template (e.g. a photo of a person [action] a/an [object]) to acquire text knowledge through the VLM text encoder. However such approaches only encoding the action-specific text prompts in vocabulary level may suffer from learning ambiguity without exploring the fine-grained clues from the perspective of interaction context. In this paper we propose a novel method to discover Syntactic Interaction Clues for HOI detection (SICHOI) by using VLM. Specifically we first investigate what are the essential elements for an interaction context and then establish a syntactic interaction bank from three levels: spatial relationship action-oriented posture and situational condition. Further to align visual features with the syntactic interaction bank we adopt a multi-view extractor to jointly aggregate visual features from instance interaction and image levels accordingly. In addition we also introduce a dual cross-attention decoder to perform context propagation between text knowledge and visual features thereby enhancing the HOI detection. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on HICO-DET and V-COCO. | [] | [] | [] | [] | 922 |
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923 | Inter-X: Towards Versatile Human-Human Interaction Analysis | Liang Xu, Xintao Lv, Yichao Yan, Xin Jin, Shuwen Wu, Congsheng Xu, Yifan Liu, Yizhou Zhou, Fengyun Rao, Xingdong Sheng, Yunhui Liu, Wenjun Zeng, Xiaokang Yang | null | The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions lack of hand gestures and fine-grained textual descriptions. To better perceive and generate human-human interactions we propose Inter-X a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns together with detailed hand gestures. The dataset includes 11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions semantic interaction categories interaction order and the relationship and personality of the subjects. Based on the elaborate annotations we propose a unified benchmark composed of 4 categories of downstream tasks from both the perceptual and generative directions. Extensive experiments and comprehensive analysis show that Inter-X serves as a testbed for promoting the development of versatile human-human interaction analysis. Our dataset and benchmark will be publicly available for research purposes. | [] | [] | [] | [] | 923 |
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924 | Generalized Predictive Model for Autonomous Driving | http://arxiv.org/abs/2403.09630 | Jiazhi Yang, Shenyuan Gao, Yihang Qiu, Li Chen, Tianyu Li, Bo Dai, Kashyap Chitta, Penghao Wu, Jia Zeng, Ping Luo, Jun Zhang, Andreas Geiger, Yu Qiao, Hongyang Li | 2,403.0963 | In this paper we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower the generalization ability of our model we acquire massive data from the web and pair it with diverse and high-quality text descriptions. The resultant dataset accumulates over 2000 hours of driving videos spanning areas all over the world with diverse weather conditions and traffic scenarios. Inheriting the merits from recent latent diffusion models our model dubbed GenAD handles the challenging dynamics in driving scenes with novel temporal reasoning blocks. We showcase that it can generalize to various unseen driving datasets in a zero-shot manner surpassing general or driving-specific video prediction counterparts. Furthermore GenAD can be adapted into an action-conditioned prediction model or a motion planner holding great potential for real-world driving applications. | [] | [] | [] | [] | 924 |
925 | FACT: Frame-Action Cross-Attention Temporal Modeling for Efficient Action Segmentation | Zijia Lu, Ehsan Elhamifar | null | We study supervised action segmentation whose goal is to predict framewise action labels of a video. To capture temporal dependencies over long horizons prior works either improve framewise features with transformer or refine framewise predictions with learned action features. However they are computationally costly and ignore that frame and action features contain complimentary information which can be leveraged to enhance both features and improve temporal modeling. Therefore we propose an efficient Frame-Action Cross-attention Temporal modeling (FACT) framework that performs temporal modeling with frame and action features in parallel and leverage this parallelism to achieve iterative bidirectional information transfer between the features and refine them. FACT network contains (i) a frame branch to learn frame-level information with convolutions and frame features (ii) an action branch to learn action-level dependencies with transformers and action tokens and (iii) cross-attentions to allow communication between the two branches. We also propose a new matching loss to ensure each action token uniquely encodes an action segment thus better captures its semantics. Thanks to our architecture we can also leverage textual transcripts of videos to help action segmentation. We evaluate FACT on four video datasets (two egocentric and two third-person) for action segmentation with and without transcripts showing that it significantly improves the state-of-the-art accuracy while enjoys lower computational cost (3 times faster) than existing transformer-based methods | [] | [] | [] | [] | 925 |
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926 | Test-Time Zero-Shot Temporal Action Localization | http://arxiv.org/abs/2404.05426 | Benedetta Liberatori, Alessandro Conti, Paolo Rota, Yiming Wang, Elisa Ricci | 2,404.05426 | Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model on a large amount of annotated training data. While effective training-based ZS-TAL approaches assume the availability of labeled data for supervised learning which can be impractical in some applications. Furthermore the training process naturally induces a domain bias into the learned model which may adversely affect the model's generalization ability to arbitrary videos. These considerations prompt us to approach the ZS-TAL problem from a radically novel perspective relaxing the requirement for training data. To this aim we introduce a novel method that performs Test-Time adaptation for Temporal Action Localization (T3AL). In a nutshell T3AL adapts a pre-trained Vision and Language Model (VLM). T3AL operates in three steps. First a video-level pseudo-label of the action category is computed by aggregating information from the entire video. Then action localization is performed adopting a novel procedure inspired by self-supervised learning. Finally frame-level textual descriptions extracted with a state-of-the-art captioning model are employed for refining the action region proposals. We validate the effectiveness of T 3AL by conducting experiments on the THUMOS14 and the ActivityNet-v1.3 datasets. Our results demonstrate that T3AL significantly outperforms zero-shot baselines based on state-of-the-art VLMs confirming the benefit of a test-time adaptation approach. | [] | [] | [] | [] | 926 |
927 | AM-RADIO: Agglomerative Vision Foundation Model Reduce All Domains Into One | Mike Ranzinger, Greg Heinrich, Jan Kautz, Pavlo Molchanov | null | A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP DINOv2 SAM are trained with distinct objectives exhibiting unique characteristics for various downstream tasks. We find that despite their conceptual differences these models can be effectively merged into a unified model through multi-teacher distillation. We name this approach AM-RADIO (Agglomerative Model -- Reduce All Domains Into One). This integrative approach not only surpasses the performance of individual teacher models but also amalgamates their distinctive features such as zero-shot vision-language comprehension detailed pixel-level understanding and open vocabulary segmentation capabilities. Additionally in pursuit of the most hardware-efficient backbone we evaluated numerous architectures in our multi-teacher distillation pipeline using the same training recipe. This led to the development of a novel architecture (E-RADIO) that exceeds the performance of its predecessors and is at least 6x faster than the teacher models at matched resolution. Our comprehensive benchmarking process covers downstream tasks including ImageNet classification semantic segmentation linear probing COCO object detection and integration into LLaVa-1.5. | [] | [] | [] | [] | 927 |
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928 | MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation | http://arxiv.org/abs/2401.07745 | Mi Yan, Jiazhao Zhang, Yan Zhu, He Wang | 2,401.07745 | Open-vocabulary 3D instance segmentation is cutting-edge for its ability to segment 3D instances without predefined categories. However progress in 3D lags behind its 2D counterpart due to limited annotated 3D data. To address this recent works first generate 2D open-vocabulary masks through 2D models and then merge them into 3D instances based on metrics calculated between two neighboring frames. In contrast to these local metrics we propose a novel metric view consensus rate to enhance the utilization of multi-view observations. The key insight is that two 2D masks should be deemed part of the same 3D instance if a significant number of other 2D masks from different views contain both these two masks. Using this metric as edge weight we construct a global mask graph where each mask is a node. Through iterative clustering of masks showing high view consensus we generate a series of clusters each representing a distinct 3D instance. Notably our model is training-free. Through extensive experiments on publicly available datasets including ScanNet++ ScanNet200 and MatterPort3D we demonstrate that our method achieves state-of-the-art performance in open-vocabulary 3D instance segmentation. Our project page is at \href https://pku-epic.github.io/MaskClustering/ https://pku-epic.github.io/MaskClustering . | [] | [] | [] | [] | 928 |
929 | Seamless Human Motion Composition with Blended Positional Encodings | http://arxiv.org/abs/2402.15509 | German Barquero, Sergio Escalera, Cristina Palmero | 2,402.15509 | Conditional human motion generation is an important topic with many applications in virtual reality gaming and robotics. While prior works have focused on generating motion guided by text music or scenes these typically result in isolated motions confined to short durations. Instead we address the generation of long continuous sequences guided by a series of varying textual descriptions. In this context we introduce FlowMDM the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this we introduce the Blended Positional Encodings a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically global motion coherence is recovered at the absolute stage whereas smooth and realistic transitions are built at the relative stage. As a result we achieve state-of-the-art results in terms of accuracy realism and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention which makes it robust against varying text descriptions at inference time. Finally to address the limitations of existing HMC metrics we propose two new metrics: the Peak Jerk and the Area Under the Jerk to detect abrupt transitions. | [] | [] | [] | [] | 929 |
930 | PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor | http://arxiv.org/abs/2403.06668 | Jaewon Jung, Hongsun Jang, Jaeyong Song, Jinho Lee | 2,403.06668 | Adversarial robustness of the neural network is a significant concern when it is applied to security-critical domains. In this situation adversarial distillation is a promising option which aims to distill the robustness of the teacher network to improve the robustness of a small student network. Previous works pretrain the teacher network to make it robust against the adversarial examples aimed at itself. However the adversarial examples are dependent on the parameters of the target network. The fixed teacher network inevitably degrades its robustness against the unseen transferred adversarial examples which target the parameters of the student network in the adversarial distillation process. We propose PeerAiD to make a peer network learn the adversarial examples of the student network instead of adversarial examples aimed at itself. PeerAiD is an adversarial distillation that trains the peer network and the student network simultaneously in order to specialize the peer network for defending the student network. We observe that such peer networks surpass the robustness of the pretrained robust teacher model against adversarial examples aimed at the student network. With this peer network and adversarial distillation PeerAiD achieves significantly higher robustness of the student network with AutoAttack (AA) accuracy by up to 1.66%p and improves the natural accuracy of the student network by up to 4.72%p with ResNet-18 on TinyImageNet dataset. Code is available at https://github.com/jaewonalive/PeerAiD. | [] | [] | [] | [] | 930 |
931 | Scaling Laws for Data Filtering-- Data Curation cannot be Compute Agnostic | Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter | null | Vision-language models (VLMs) are trained for thousands of GPU hours on carefully selected subsets of massive web scrapes. For instance the LAION public dataset retained only about 10 percent of the total crawled data. In recent times data curation has gained prominence with several works developing strategies to retain high-quality subsets of raw scraped data. However these strategies are typically developed agnostic to the available compute for training. In this paper we demonstrate that making filtering decisions independent of training compute is often suboptimal: well-curated data rapidly loses its utility when repeated eventually decreasing below the utility of unseen but lower-quality data. While past research in neural scaling laws has considered web data to be homogenous real data is not. Our work bridges this important gap in the literature by developing scaling laws that characterize the differing utility of various data subsets and accounting for how this diminishes for a data point at its nth repetition. Our key message is that data curation can not be agnostic of the total compute a model will be trained for. Even without ever jointly training on multiple data buckets our scaling laws enable us to estimate model performance under this dynamic trade-off between quality and repetition. This allows us to curate the best possible pool for achieving top performance on Datacomp at various compute budgets carving out a pareto-frontier for data curation. | [] | [] | [] | [] | 931 |
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932 | FastMAC: Stochastic Spectral Sampling of Correspondence Graph | http://arxiv.org/abs/2403.08770 | Yifei Zhang, Hao Zhao, Hongyang Li, Siheng Chen | 2,403.0877 | 3D correspondence i.e. a pair of 3D points is a fundamental concept in computer vision. A set of 3D correspondences when equipped with compatibility edges forms a correspondence graph. This graph is a critical component in several state-of-the-art 3D point cloud registration approaches e.g. the one based on maximal cliques (MAC). However its properties have not been well understood. So we present the first study that introduces graph signal processing into the domain of correspondence graph. We exploit the generalized degree signal on correspondence graph and pursue sampling strategies that preserve high-frequency components of this signal. To address time-consuming singular value decomposition in deterministic sampling we resort to a stochastic approximate sampling strategy. As such the core of our method is the stochastic spectral sampling of correspondence graph. As an application we build a complete 3D registration algorithm termed as FastMAC that reaches real-time speed while leading to little to none performance drop. Through extensive experiments we validate that FastMAC works for both indoor and outdoor benchmarks. For example FastMAC can accelerate MAC by 80 times while maintaining high registration success rate on KITTI. Codes are publicly available at https://github.com/Forrest-110/FastMAC. | [] | [] | [] | [] | 932 |
933 | FedUV: Uniformity and Variance for Heterogeneous Federated Learning | http://arxiv.org/abs/2402.18372 | Ha Min Son, Moon-Hyun Kim, Tai-Myoung Chung, Chao Huang, Xin Liu | 2,402.18372 | Federated learning is a promising framework to train neural networks with widely distributed data. However performance degrades heavily with heterogeneously distributed data. Recent work has shown this is due to the final layer of the network being most prone to local bias some finding success freezing the final layer as an orthogonal classifier. We investigate the training dynamics of the classifier by applying SVD to the weights motivated by the observation that freezing weights results in constant singular values. We find that there are differences when training in IID and non-IID settings. Based on this finding we introduce two regularization terms for local training to continuously emulate IID settings: (1) variance in the dimension-wise probability distribution of the classifier and (2) hyperspherical uniformity of representations of the encoder. These regularizations promote local models to act as if it were in an IID setting regardless of the local data distribution thus offsetting proneness to bias while being flexible to the data. On extensive experiments in both label-shift and feature-shift settings we verify that our method achieves highest performance by a large margin especially in highly non-IID cases in addition to being scalable to larger models and datasets. | [] | [] | [] | [] | 933 |
934 | FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning | http://arxiv.org/abs/2308.12532 | Gihun Lee, Minchan Jeong, Sangmook Kim, Jaehoon Oh, Se-Young Yun | 2,308.12532 | Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge it can also hinder local learning by interfering with local objectives. Inspired by Continual Learning (CL) we adopt an orthogonal learning strategy to balance these two conflicting objectives. However we observe that directly negating the proximal gradient in the local gradient significantly undermines local learning. To address the problem we propose a novel method Federated Stabilized Orthogonal Learning (FedSOL). FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios. | [] | [] | [] | [] | 934 |
935 | GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning | http://arxiv.org/abs/2312.11461 | Ye Yuan, Xueting Li, Yangyi Huang, Shalini De Mello, Koki Nagano, Jan Kautz, Umar Iqbal | 2,312.11461 | Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions addressing the limitations (e.g. efficiency and flexibility) imposed by mesh or NeRF-based representations. However a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animations. Second to stabilize and amortize the learning of millions of Gaussians we propose to use implicit neural fields to predict the Gaussian attributes (e.g. colors). Finally to capture fine avatar geometries and extract detailed meshes we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method GAvatar enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality and achieves extremely fast rendering (100 fps) at 1K resolution. | [] | [] | [] | [] | 935 |
936 | Beyond Average: Individualized Visual Scanpath Prediction | http://arxiv.org/abs/2404.12235 | Xianyu Chen, Ming Jiang, Qi Zhao | 2,404.12235 | Understanding how attention varies across individuals has significant scientific and societal impacts. However existing visual scanpath models treat attention uniformly neglecting individual differences. To bridge this gap this paper focuses on individualized scanpath prediction (ISP) a new attention modeling task that aims to accurately predict how different individuals shift their attention in diverse visual tasks. It proposes an ISP method featuring three novel technical components: (1) an observer encoder to characterize and integrate an observer's unique attention traits (2) an observer-centric feature integration approach that holistically combines visual features task guidance and observer-specific characteristics and (3) an adaptive fixation prioritization mechanism that refines scanpath predictions by dynamically prioritizing semantic feature maps based on individual observers' attention traits. These novel components allow scanpath models to effectively address the attention variations across different observers. Our method is generally applicable to different datasets model architectures and visual tasks offering a comprehensive tool for transforming general scanpath models into individualized ones. Comprehensive evaluations using value-based and ranking-based metrics verify the method's effectiveness and generalizability. | [] | [] | [] | [] | 936 |
937 | A Category Agnostic Model for Visual Rearrangment | Yuyi Liu, Xinhang Song, Weijie Li, Xiaohan Wang, Shuqiang Jiang | null | This paper presents a novel category agnostic model for visual rearrangement task which can help an embodied agent to physically recover the shuffled scene configuration without any category concepts to the goal configuration. Previous methods usually follow a similar architecture completing the rearrangement task by aligning the scene changes of the goal and shuffled configuration according to the semantic scene graphs. However constructing scene graphs requires the inference of category labels which not only causes the accuracy drop of the entire task but also limits the application in real world scenario. In this paper we delve deep into the essence of visual rearrangement task and focus on the two most essential issues scene change detection and scene change matching. We utilize the movement and the protrusion of point cloud to accurately identify the scene changes and match these changes depending on the similarity of category agnostic appearance feature. Moreover to assist the agent to explore the environment more efficiently and comprehensively we propose a closer-aligned-retrace exploration policy aiming to observe more details of the scene at a closer distance. We conduct extensive experiments on AI2THOR Rearrangement Challenge based on RoomR dataset and a new multi-room multi-instance dataset MrMiR collected by us. The experimental results demonstrate the effectiveness of our proposed method. | [] | [] | [] | [] | 937 |
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938 | Grounding Everything: Emerging Localization Properties in Vision-Language Transformers | http://arxiv.org/abs/2312.00878 | Walid Bousselham, Felix Petersen, Vittorio Ferrari, Hilde Kuehne | 2,312.00878 | Vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval classification or captioning. But so far those models seem to fall behind when it comes to zero-shot localization of referential expressions and objects in images. As a result they need to be fine-tuned for this task. In this paper we show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. To leverage those capabilities we propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery to a self-self attention path. We show that the concept of self-self attention corresponds to clustering thus enforcing groups of tokens arising from the same object to be similar while preserving the alignment with the language space. To further guide the group formation we propose a set of regularizations that allows the model to finally generalize across datasets and backbones. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation. GEM not only outperforms other training-free open-vocabulary localization methods but also achieves state-of-the-art results on the recently proposed OpenImagesV7 large-scale segmentation benchmark. Code is available at https://github.com/WalBouss/GEM | [] | [] | [] | [] | 938 |
939 | Seeing Motion at Nighttime with an Event Camera | http://arxiv.org/abs/2404.11884 | Haoyue Liu, Shihan Peng, Lin Zhu, Yi Chang, Hanyu Zhou, Luxin Yan | 2,404.11884 | We focus on a very challenging task: imaging at nighttime dynamic scenes. Most previous methods rely on the low-light enhancement of a conventional RGB camera. However they would inevitably face a dilemma between the long exposure time of nighttime and the motion blur of dynamic scenes. Event cameras react to dynamic changes with higher temporal resolution (microsecond) and higher dynamic range (120dB) offering an alternative solution. In this work we present a novel nighttime dynamic imaging method with an event camera. Specifically we discover that the event at nighttime exhibits temporal trailing characteristics and spatial non-stationary distribution. Consequently we propose a nighttime event reconstruction network (NER-Net) which mainly includes a learnable event timestamps calibration module (LETC) to align the temporal trailing events and a non-uniform illumination aware module (NIAM) to stabilize the spatiotemporal distribution of events. Moreover we construct a paired real low-light event dataset (RLED) through a co-axial imaging system including 64200 spatially and temporally aligned image GTs and low-light events. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of visual quality and generalization ability on real-world nighttime datasets. The project are available at: https://github.com/Liu-haoyue/NER-Net. | [] | [] | [] | [] | 939 |
940 | Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability Composability and Decomposability from Anatomy via Self Supervision | http://arxiv.org/abs/2404.15672 | Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang | 2,404.15672 | Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep learning excels in learning multi-level feature spaces but they often lack explicit coding of part-whole relations a prominent property of medical imaging. To overcome this limitation we introduce Adam-v2 a new self-supervised learning framework extending Adam [68] by explicitly incorporating part-whole hierarchies into its learning objectives through three key branches: (1) Localizability acquiring discriminative representations to distinguish different anatomical patterns; (2) Composability learning each anatomical structure in a parts-to-whole manner; and (3) Decomposability comprehending each anatomical structure in a whole-to-parts manner. Experimental results across 10 tasks compared to 11 baselines in zero-shot few-shot transfer and full fine-tuning settings showcase Adam-v2's superior performance over large-scale medical models and existing SSL methods across diverse downstream tasks. The higher generality and robustness of Adam-v2's representations originate from its explicit construction of hierarchies for distinct anatomical structures from unlabeled medical images. Adam-v2 preserves a semantic balance of anatomical diversity and harmony in its embedding yielding representations that are both generic and semantically meaningful yet overlooked in existing SSL methods. All code and pretrained models are available at GitHub.com/JLiangLab/Eden. | [] | [] | [] | [] | 940 |
941 | Efficient Test-Time Adaptation of Vision-Language Models | http://arxiv.org/abs/2403.18293 | Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El Saddik, Eric Xing | 2,403.18293 | Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance they involve intensive computation which is severely unaligned with test-time adaptation. We design TDA a training-free dynamic adapter that enables effective and efficient test-time adaptation with vision-language models. TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys. Leveraging the key-value cache TDA allows adapting to test data gradually via progressive pseudo label refinement which is super-efficient without incurring any backpropagation. In addition we introduce negative pseudo labeling that alleviates the adverse impact of pseudo label noises by assigning pseudo labels to certain negative classes when the model is uncertain about its pseudo label predictions. Extensive experiments over two benchmarks demonstrate TDA's superior effectiveness and efficiency as compared with the state-of-the-art. The code has been released in https://kdiaaa.github.io/tda/. | [] | [] | [] | [] | 941 |
942 | Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs | http://arxiv.org/abs/2401.06209 | Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, Saining Xie | 2,401.06209 | Is vision good enough for language? Recent advancements in multimodal models primarily stem from the powerful reasoning abilities of large language models (LLMs). However the visual component typically depends only on the instance-level contrastive language-image pre-training (CLIP). Our research reveals that the visual capabilities in recent MultiModal LLMs (MLLMs) still exhibit systematic shortcomings. To understand the roots of these errors we explore the gap between the visual embedding space of CLIP and vision-only self-supervised learning. We identify "CLIP-blind pairs" - images that CLIP perceives as similar despite their clear visual differences. With these pairs we construct the Multimodal Visual Patterns (MMVP) benchmark. MMVP exposes areas where state-of-the-art systems including GPT-4V struggle with straightforward questions across nine basic visual patterns often providing incorrect answers and hallucinated explanations. We further evaluate various CLIP-based vision-and-language models and found a notable correlation between visual patterns that challenge CLIP models and those problematic for multimodal LLMs. As an initial effort to address these issues we propose a Mixture of Features (MoF) approach demonstrating that integrating vision self-supervised learning features with MLLMs can significantly enhance their visual grounding capabilities. Together our research suggests visual representation learning remains an open challenge and accurate visual grounding is crucial for future successful multimodal systems. | [] | [] | [] | [] | 942 |
943 | Mean-Shift Feature Transformer | Takumi Kobayashi | null | Transformer models developed in NLP make a great impact on computer vision fields producing promising performance on various tasks. While multi-head attention a characteristic mechanism of the transformer attracts keen research interest such as for reducing computation cost we analyze the transformer model from a viewpoint of feature transformation based on a distribution of input feature tokens. The analysis inspires us to derive a novel transformation method from mean-shift update which is an effective gradient ascent to seek a local mode of distinctive representation on the token distribution. We also present an efficient projection approach to reduce parameter size of linear projections constituting the proposed multi-head feature transformation. In the experiments on ImageNet-1K dataset the proposed methods embedded into various network models exhibit favorable performance improvement in place of the transformer module. | [] | [] | [] | [] | 943 |
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944 | Domain Separation Graph Neural Networks for Saliency Object Ranking | Zijian Wu, Jun Lu, Jing Han, Lianfa Bai, Yi Zhang, Zhuang Zhao, Siyang Song | null | Saliency object ranking (SOR) has attracted significant attention recently. Previous methods usually failed to explicitly explore the saliency degree-related relationships between objects. In this paper we propose a novel Domain Separation Graph Neural Network (DSGNN) which starts with separately extracting the shape and texture cues from each object and builds an shape graph as well as a texture graph for all objects in the given image. Then we propose a Shape-Texture Graph Domain Separation (STGDS) module to separate the task-relevant and irrelevant information of target objects by explicitly modelling the relationship between each pair of objects in terms of their shapes and textures respectively. Furthermore a Cross Image Graph Domain Separation (CIGDS) module is introduced to explore the saliency degree subspace that is robust to different scenes aiming to create a unified representation for targets with the same saliency levels in different images. Importantly our DSGNN automatically learns a multi-dimensional feature to represent each graph edge allowing complex diverse and ranking-related relationships to be modelled. Experimental results show that our DSGNN achieved the new state-of-the-art performance on both ASSR and IRSR datasets with large improvements of 5.2% and 4.1% SA-SOR respectively. Our code is provided in https://github.com/Wu-ZJ/DSGNN. | [] | [] | [] | [] | 944 |
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945 | Mind Marginal Non-Crack Regions: Clustering-Inspired Representation Learning for Crack Segmentation | Zhuangzhuang Chen, Zhuonan Lai, Jie Chen, Jianqiang Li | null | Crack segmentation datasets make great efforts to obtain the ground truth crack or non-crack labels as clearly as possible. However it can be observed that ambiguities are still inevitable when considering the marginal non-crack region due to low contrast and heterogeneous texture. To solve this problem we propose a novel clustering-inspired representation learning framework which contains a two-phase strategy for automatic crack segmentation. In the first phase a pre-process is proposed to localize the marginal non-crack region. Then we propose an ambiguity-aware segmentation loss (Aseg Loss) that enables crack segmentation models to capture ambiguities in the above regions via learning segmentation variance which allows us to further localize ambiguous regions. In the second phase to learn the discriminative features of the above regions we propose a clustering-inspired loss (CI Loss) that alters the supervision learning of these regions into an unsupervised clustering manner. We demonstrate that the proposed method could surpass the existing crack segmentation models on various datasets and our constructed CrackSeg5k dataset. | [] | [] | [] | [] | 945 |
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946 | FISBe: A Real-World Benchmark Dataset for Instance Segmentation of Long-Range Thin Filamentous Structures | http://arxiv.org/abs/2404.00130 | Lisa Mais, Peter Hirsch, Claire Managan, Ramya Kandarpa, Josef Lorenz Rumberger, Annika Reinke, Lena Maier-Hein, Gudrun Ihrke, Dagmar Kainmueller | 2,404.0013 | Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging thin filamentous and widely branching morphologies multiple neurons are tightly inter-weaved and partial volume effects uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing to date methods are typically benchmarked on synthetic datasets. To address this gap we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies and facilitate scientific discovery in basic neuroscience. | [] | [] | [] | [] | 946 |
947 | RegionGPT: Towards Region Understanding Vision Language Model | http://arxiv.org/abs/2403.02330 | Qiushan Guo, Shalini De Mello, Hongxu Yin, Wonmin Byeon, Ka Chun Cheung, Yizhou Yu, Ping Luo, Sifei Liu | 2,403.0233 | Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder and the use of coarse-grained training data that lacks detailed region-specific captions. To address this we introduce RegionGPT (short as RGPT) a novel framework designed for complex region-level captioning and understanding. RGPT enhances the spatial awareness of regional representation with simple yet effective modifications to existing visual encoders in VLMs. We further improve performance on tasks requiring a specific output scope by integrating task-guided instruction prompts during both training and inference phases while maintaining the model's versatility for general-purpose tasks. Additionally we develop an automated region caption data generation pipeline enriching the training set with detailed region-level captions. We demonstrate that a universal RGPT model can be effectively applied and significantly enhancing performance across a range of region-level tasks including but not limited to complex region descriptions reasoning object classification and referring expressions comprehension. | [] | [] | [] | [] | 947 |
948 | LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding Reasoning and Planning | http://arxiv.org/abs/2311.18651 | Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, Tao Chen | 2,311.18651 | Recent progress in Large Multimodal Models (LMM) has opened up great possibilities for various applications in the field of human-machine interactions. However developing LMMs that can comprehend reason and plan in complex and diverse 3D environments remains a challenging topic especially considering the demand for understanding permutation-invariant point cloud representations of the 3D scene. Existing works seek help from multi-view images by projecting 2D features to 3D space which inevitably leads to huge computational overhead and performance degradation. In this paper we present LL3DA a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions. The additional visual interaction enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts. Experiments show that LL3DA achieves remarkable results and surpasses various 3D vision-language models on both 3D Dense Captioning and 3D Question Answering. | [] | [] | [] | [] | 948 |
949 | 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering | http://arxiv.org/abs/2310.08528 | Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, Xinggang Wang | 2,310.08528 | Representing and rendering dynamic scenes has been an important but challenging task. Especially to accurately model complex motions high efficiency is usually hard to guarantee. To achieve real-time dynamic scene rendering while also enjoying high training and storage efficiency we propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame. In 4D-GS a novel explicit representation containing both 3D Gaussians and 4D neural voxels is proposed. A decomposed neural voxel encoding algorithm inspired by HexPlane is proposed to efficiently build Gaussian features from 4D neural voxels and then a lightweight MLP is applied to predict Gaussian deformations at novel timestamps. Our 4D-GS method achieves real-time rendering under high resolutions 82 FPS at an 800*800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state-of-the-art methods. More demos and code are available at https://guanjunwu.github.io/4dgs. | [] | [] | [] | [] | 949 |
950 | RAM-Avatar: Real-time Photo-Realistic Avatar from Monocular Videos with Full-body Control | Xiang Deng, Zerong Zheng, Yuxiang Zhang, Jingxiang Sun, Chao Xu, Xiaodong Yang, Lizhen Wang, Yebin Liu | null | This paper focuses on advancing the applicability of human avatar learning methods by proposing RAM-Avatar which learns a Real-time photo-realistic Avatar that supports full-body control from Monocular videos. To achieve this goal RAM-Avatar leverages two statistical templates responsible for modeling the facial expression and hand gesture variations while a sparsely computed dual attention module is introduced upon another body template to facilitate high-fidelity texture rendering for the torsos and limbs. Building on this foundation we deploy a lightweight yet powerful StyleUnet along with a temporal-aware discriminator to achieve real-time realistic rendering. To enable robust animation for out-of-distribution poses we propose a Motion Distribution Align module to compensate for the discrepancies between the training and testing motion distribution. Results and extensive experiments conducted in various experimental settings demonstrate the superiority of our proposed method and a real-time live system is proposed to further push research into applications. The training and testing code will be released for research purposes. | [] | [] | [] | [] | 950 |
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951 | Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching | Xianqi Wang, Gangwei Xu, Hao Jia, Xin Yang | null | Stereo matching methods based on iterative optimization like RAFT-Stereo and IGEV-Stereo have evolved into a cornerstone in the field of stereo matching. However these methods struggle to simultaneously capture high-frequency information in edges and low-frequency information in smooth regions due to the fixed receptive field. As a result they tend to lose details blur edges and produce false matches in textureless areas. In this paper we propose Selective Recurrent Unit (SRU) a novel iterative update operator for stereo matching. The SRU module can adaptively fuse hidden disparity information at multiple frequencies for edge and smooth regions. To perform adaptive fusion we introduce a new Contextual Spatial Attention (CSA) module to generate attention maps as fusion weights. The SRU empowers the network to aggregate hidden disparity information across multiple frequencies mitigating the risk of vital hidden disparity information loss during iterative processes. To verify SRU's universality we apply it to representative iterative stereo matching methods collectively referred to as Selective-Stereo. Our Selective-Stereo ranks first on KITTI 2012 KITTI 2015 ETH3D and Middlebury leaderboards among all published methods. Code is available at https://github.com/Windsrain/Selective-Stereo. | [] | [] | [] | [] | 951 |
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952 | PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees | http://arxiv.org/abs/2302.06637 | Chulin Xie, De-An Huang, Wenda Chu, Daguang Xu, Chaowei Xiao, Bo Li, Anima Anandkumar | 2,302.06637 | Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However existing pFL methods either (1) introduce high computation and communication costs or (2) overfit to local data which can be limited in scope and vulnerable to evolved test samples with natural distribution shifts. In this paper we propose PerAda a parameter-efficient pFL framework that reduces communication and computational costs and exhibits superior generalization performance especially under test-time distribution shifts. PerAda reduces the costs by leveraging the power of pretrained models and only updates and communicates a small number of additional parameters from adapters. PerAda achieves high generalization by regularizing each client's personalized adapter with a global adapter while the global adapter uses knowledge distillation to aggregate generalized information from all clients. Theoretically we provide generalization bounds of PerAda and we prove its convergence to stationary points under non-convex settings. Empirically PerAda demonstrates higher personalized performance (+4.85% on CheXpert) and enables better out-of-distribution generalization (+5.23% on CIFAR-10-C) on different datasets across natural and medical domains compared with baselines while only updating 12.6% of parameters per model. Our code is available at https://github.com/NVlabs/PerAda. | [] | [] | [] | [] | 952 |
953 | MAFA: Managing False Negatives for Vision-Language Pre-training | Jaeseok Byun, Dohoon Kim, Taesup Moon | null | We consider a critical issue of false negatives in Vision- Language Pre-training (VLP) a challenge that arises from the inherent many-to-many correspondence of image-text pairs in large-scale web-crawled datasets. The presence of false negatives can impede achieving optimal performance and even lead to a significant performance drop. To address this challenge we propose MAFA (MAnaging FAlse nega- tives) which consists of two pivotal components building upon the recently developed GRouped mIni-baTch sampling (GRIT) strategy: 1) an efficient connection mining process that identifies and converts false negatives into positives and 2) label smoothing for the image-text contrastive (ITC) loss. Our comprehensive experiments verify the effectiveness of MAFA across multiple downstream tasks emphasizing the crucial role of addressing false negatives in VLP potentially even surpassing the importance of addressing false posi- tives. In addition the compatibility of MAFA with the recent BLIP-family model is also demonstrated. Code is available at https://github.com/jaeseokbyun/MAFA. | [] | [] | [] | [] | 953 |
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954 | Video Prediction by Modeling Videos as Continuous Multi-Dimensional Processes | Gaurav Shrivastava, Abhinav Shrivastava | null | Diffusion models have made significant strides in image generation mastering tasks such as unconditional image synthesis text-image translation and image-to-image conversions. However their capability falls short in the realm of video prediction mainly because they treat videos as a collection of independent images relying on external constraints such as temporal attention mechanisms to enforce temporal coherence. In our paper we introduce a novel model class that treats video as a continuous multi-dimensional process rather than a series of discrete frames. Through extensive experimentation we establish state-of-the-art performance in video prediction validated on benchmark datasets including KTH BAIR Human3.6M and UCF101. | [] | [] | [] | [] | 954 |
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955 | PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns | http://arxiv.org/abs/2312.04534 | Shuliang Ning, Duomin Wang, Yipeng Qin, Zirong Jin, Baoyuan Wang, Xiaoguang Han | 2,312.04534 | In this paper we propose a novel virtual try-on from unconstrained designs (ucVTON) task to enable photorealistic synthesis of personalized composite clothing on input human images. Unlike prior arts constrained by specific input types our method allows flexible specification of style (text or image) and texture (full garment cropped sections or texture patches) conditions. To address the entanglement challenge when using full garment images as conditions we develop a two-stage pipeline with explicit disentanglement of style and texture. In the first stage we generate a human parsing map reflecting the desired style conditioned on the input. In the second stage we composite textures onto the parsing map areas based on the texture input. To represent complex and non-stationary textures that have never been achieved in previous fashion editing works we first propose extracting hierarchical and balanced CLIP features and applying position encoding in VTON. Experiments demonstrate superior synthesis quality and personalization enabled by our method. The flexible control over style and texture mixing brings virtual try-on to a new level of user experience for online shopping and fashion design. | [] | [] | [] | [] | 955 |
956 | InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning | http://arxiv.org/abs/2404.00228 | Yan-Shuo Liang, Wu-Jun Li | 2,404.00228 | Continual learning requires the model to learn multiple tasks sequentially. In continual learning the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently parameter-efficient fine-tuning (PEFT) which involves freezing a pre-trained model and injecting a small number of learnable parameters to adapt to downstream tasks has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT most of them do not consider how to eliminate the interference of the new task on the old tasks which inhibits the model from making a good trade-off between stability and plasticity. In this work we propose a new PEFT method called interference-free low-rank adaptation (InfLoRA) for continual learning. InfLoRA injects a small number of parameters to reparameterize the pre-trained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pre-trained weights within a subspace. Furthermore InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks making a good trade-off between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets. | [] | [] | [] | [] | 956 |
957 | Towards Robust 3D Pose Transfer with Adversarial Learning | http://arxiv.org/abs/2404.02242 | Haoyu Chen, Hao Tang, Ehsan Adeli, Guoying Zhao | 2,404.02242 | 3D pose transfer that aims to transfer the desired pose to a target mesh is one of the most challenging 3D generation tasks. Previous attempts rely on well-defined parametric human models or skeletal joints as driving pose sources. However to obtain those clean pose sources cumbersome but necessary pre-processing pipelines are inevitable hindering implementations of the real-time applications. This work is driven by the intuition that the robustness of the model can be enhanced by introducing adversarial samples into the training leading to a more invulnerable model to the noisy inputs which even can be further extended to directly handling the real-world data like raw point clouds/scans without intermediate processing. Furthermore we propose a novel 3D pose Masked Autoencoder (3D-PoseMAE) a customized MAE that effectively learns 3D extrinsic presentations (i.e. pose). 3D-PoseMAE facilitates learning from the aspect of extrinsic attributes by simultaneously generating adversarial samples that perturb the model and learning the arbitrary raw noisy poses via a multi-scale masking strategy. Both qualitative and quantitative studies show that the transferred meshes given by our network result in much better quality. Besides we demonstrate the strong generalizability of our method on various poses different domains and even raw scans. Experimental results also show meaningful insights that the intermediate adversarial samples generated in the training can successfully attack the existing pose transfer models. | [] | [] | [] | [] | 957 |
958 | Error Detection in Egocentric Procedural Task Videos | Shih-Po Lee, Zijia Lu, Zekun Zhang, Minh Hoai, Ehsan Elhamifar | null | We present a new egocentric procedural error dataset containing videos with various types of errors as well as normal videos and propose a new framework for procedural error detection using error-free training videos only. Our framework consists of an action segmentation model and a contrastive step prototype learning module to segment actions and learn useful features for error detection. Based on the observation that interactions between hands and objects often inform action and error understanding we propose to combine holistic frame features with relations features which we learn by building a graph using active object detection followed by a Graph Convolutional Network. To handle errors unseen during training we use our contrastive step prototype learning to learn multiple prototypes for each step capturing variations of error-free step executions. At inference time we use feature-prototype similarities for error detection. By experiments on three datasets we show that our proposed framework outperforms state-of-the-art video anomaly detection methods for error detection and provides smooth action and error predictions. | [] | [] | [] | [] | 958 |
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959 | EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation | http://arxiv.org/abs/2403.01482 | Chanyoung Kim, Woojung Han, Dayun Ju, Seong Jae Hwang | 2,403.01482 | Semantic segmentation has innately relied on extensive pixel-level annotated data leading to the emergence of unsupervised methodologies. Among them leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has been making steady progress with expressive deep features. Yet for semantically segmenting images with complex objects a predominant challenge remains: the lack of explicit object-level semantic encoding in patch-level features. This technical limitation often leads to inadequate segmentation of complex objects with diverse structures. To address this gap we present a novel approach EAGLE which emphasizes object-centric representation learning for unsupervised semantic segmentation. Specifically we introduce EiCue a spectral technique providing semantic and structural cues through an eigenbasis derived from the semantic similarity matrix of deep image features and color affinity from an image. Further by incorporating our object-centric contrastive loss with EiCue we guide our model to learn object-level representations with intra- and inter-image object-feature consistency thereby enhancing semantic accuracy. Extensive experiments on COCO-Stuff Cityscapes and Potsdam-3 datasets demonstrate the state-of-the-art USS results of EAGLE with accurate and consistent semantic segmentation across complex scenes. | [] | [] | [] | [] | 959 |
960 | AVID: Any-Length Video Inpainting with Diffusion Model | http://arxiv.org/abs/2312.03816 | Zhixing Zhang, Bichen Wu, Xiaoyan Wang, Yaqiao Luo, Luxin Zhang, Yinan Zhao, Peter Vajda, Dimitris Metaxas, Licheng Yu | 2,312.03816 | Recent advances in diffusion models have successfully enabled text-guided image inpainting. While it seems straightforward to extend such editing capability into the video domain there have been fewer works regarding text-guided video inpainting. Given a video a masked region at its initial frame and an editing prompt it requires a model to do infilling at each frame following the editing guidance while keeping the out-of-mask region intact. There are three main challenges in text-guided video inpainting: (i) temporal consistency of the edited video (ii) supporting different inpainting types at different structural fidelity levels and (iii) dealing with variable video length. To address these challenges we introduce Any-Length Video Inpainting with Diffusion Model dubbed as AVID. At its core our model is equipped with effective motion modules and adjustable structure guidance for fixed-length video inpainting. Building on top of that we propose a novel Temporal MultiDiffusion sampling pipeline with a middle-frame attention guidance mechanism facilitating the generation of videos with any desired duration. Our comprehensive experiments show our model can robustly deal with various inpainting types at different video duration ranges with high quality. | [] | [] | [] | [] | 960 |
961 | NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging | http://arxiv.org/abs/2403.03485 | Takahiro Shirakawa, Seiichi Uchida | 2,403.03485 | Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-image diffusion models still have several issues including mismatches between the text and layout conditions and quality degradation of generated images. This paper proposes a novel layout-aware text-to-image diffusion model called NoiseCollage to tackle these issues. During the denoising process NoiseCollage independently estimates noises for individual objects and then crops and merges them into a single noise. This operation helps avoid condition mismatches; in other words it can put the right objects in the right places. Qualitative and quantitative evaluations show that NoiseCollage outperforms several state-of-the-art models. These successful results indicate that the crop-and-merge operation of noises is a reasonable strategy to control image generation. We also show that NoiseCollage can be integrated with ControlNet to use edges sketches and pose skeletons as additional conditions. Experimental results show that this integration boosts the layout accuracy of ControlNet. The code is available at https://github.com/univ-esuty/noisecollage. | [] | [] | [] | [] | 961 |
962 | Uncertainty-Guided Never-Ending Learning to Drive | Lei Lai, Eshed Ohn-Bar, Sanjay Arora, John Seon Keun Yi | null | We present a highly scalable self-training framework for incrementally adapting vision-based end-to-end autonomous driving policies in a semi-supervised manner i.e. over a continual stream of incoming video data. To facilitate large-scale model training (e.g. open web or unlabeled data) we do not assume access to ground-truth labels and instead estimate pseudo-label policy targets for each video. Our framework comprises three key components: knowledge distillation a sample purification module and an exploration and knowledge retention mechanism. First given sequential image frames we pseudo-label the data and estimate uncertainty using an ensemble of inverse dynamics models. The uncertainty is used to select the most informative samples to add to an experience replay buffer. We specifically select high-uncertainty pseudo-labels to facilitate the exploration and learning of new and diverse driving skills. However in contrast to prior work in continual learning that assumes ground-truth labeled samples the uncertain pseudo-labels can introduce significant noise. Thus we also pair the exploration with a label refinement module which makes use of consistency constraints to re-label the noisy exploratory samples and effectively learn from diverse data. Trained as a complete never-ending learning system we demonstrate state-of-the-art performance on training from domain-changing data as well as millions of images from the open web. | [] | [] | [] | [] | 962 |
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963 | FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusion | George Cazenavette, Avneesh Sud, Thomas Leung, Ben Usman | null | Due to the high potential for abuse of GenAI systems the task of detecting synthetic images has recently become of great interest to the research community. Unfortunately existing image space detectors quickly become obsolete as new high-fidelity text-to-image models are developed at blinding speed. In this work we propose a new synthetic image detector that uses features obtained by inverting an open-source pre-trained Stable Diffusion model. We show that these inversion features enable our detector to generalize well to unseen generators of high visual fidelity (e.g. DALL*E 3) even when the detector is trained only on lower fidelity fake images generated via Stable Diffusion. This detector achieves new state-of-the-art across multiple training and evaluation setups. Moreover we introduce a new challenging evaluation protocol that uses reverse image search to mitigate stylistic and thematic biases in the detector evaluation. We show that the resulting evaluation scores align well with detectors' in-the-wild performance and release these datasets as public benchmarks for future research. | [] | [] | [] | [] | 963 |
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964 | PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment | http://arxiv.org/abs/2312.09866 | Tianchen Deng, Guole Shen, Tong Qin, Jianyu Wang, Wentao Zhao, Jingchuan Wang, Danwei Wang, Weidong Chen | 2,312.09866 | Neural implicit scene representations have recently shown encouraging results in dense visual SLAM. However existing methods produce low-quality scene reconstruction and low-accuracy localization performance when scaling up to large indoor scenes and long sequences. These limitations are mainly due to their single global radiance field with finite capacity which does not adapt to large scenarios. Their end-to-end pose networks are also not robust enough with the growth of cumulative errors in large scenes. To this end we introduce PLGSLAM a neural visual SLAM system capable of high-fidelity surface reconstruction and robust camera tracking in real-time. To handle large-scale indoor scenes PLGSLAM proposes a progressive scene representation method which dynamically allocates new local scene representation trained with frames within a local sliding window. This allows us to scale up to larger indoor scenes and improves robustness (even under pose drifts). In local scene representation PLGSLAM utilizes tri-planes for local high-frequency features with multi-layer perceptron (MLP) networks for the low-frequency feature achieving smoothness and scene completion in unobserved areas. Moreover we propose local-to-global bundle adjustment method with a global keyframe database to address the increased pose drifts on long sequences. Experimental results demonstrate that PLGSLAM achieves state-of-the-art scene reconstruction results and tracking performance across various datasets and scenarios (both in small and large-scale indoor environments). | [] | [] | [] | [] | 964 |
965 | Multi-Task Dense Prediction via Mixture of Low-Rank Experts | http://arxiv.org/abs/2403.17749 | Yuqi Yang, Peng-Tao Jiang, Qibin Hou, Hao Zhang, Jinwei Chen, Bo Li | 2,403.17749 | Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global relations among all tasks. In this paper we present a novel decoder-focused method for multi-task dense prediction called Mixture-of-Low-Rank-Experts (MLoRE). To model the global task relationships MLoRE adds a generic convolution path to the original MoE structure where each task feature can go through this path for explicit parameter sharing. Furthermore to control the parameters and computational cost brought by the increase in the number of experts we take inspiration from LoRA and propose to leverage the low-rank format of a vanilla convolution in the expert network. Since the low-rank experts have fewer parameters and can be dynamically parameterized into the generic convolution the parameters and computational cost do not change much with the increase of experts. Benefiting from this design we increase the number of experts and its reception field to enlarge the representation capacity facilitating multiple dense tasks learning in a unified network. Extensive experiments on the PASCAL-Context and NYUD-v2 benchmarks show that our MLoRE achieves superior performance compared to previous state-of-the-art methods on all metrics. Our code is available at https://github.com/YuqiYang213/MLoRE. | [] | [] | [] | [] | 965 |
966 | Binding Touch to Everything: Learning Unified Multimodal Tactile Representations | http://arxiv.org/abs/2401.18084 | Fengyu Yang, Chao Feng, Ziyang Chen, Hyoungseob Park, Daniel Wang, Yiming Dou, Ziyao Zeng, Xien Chen, Rit Gangopadhyay, Andrew Owens, Alex Wong | 2,401.18084 | The ability to associate touch with other modalities has huge implications for humans and computational systems. However multimodal learning with touch remains challenging due to the expensive data collection process and non-standardized sensor outputs. We introduce UniTouch a unified tactile model for vision-based touch sensors connected to multiple modalities including vision language and sound. We achieve this by aligning our UniTouch embeddings to pretrained image embeddings already associated with a variety of other modalities. We further propose learnable sensor-specific tokens allowing the model to learn from a set of heterogeneous tactile sensors all at the same time. UniTouch is capable of conducting various touch sensing tasks in the zero-shot setting from robot grasping prediction to touch image question answering. To the best of our knowledge UniTouch is the first to demonstrate such capabilities. | [] | [] | [] | [] | 966 |
967 | Attribute-Guided Pedestrian Retrieval: Bridging Person Re-ID with Internal Attribute Variability | Yan Huang, Zhang Zhang, Qiang Wu, Yi Zhong, Liang Wang | null | In various domains such as surveillance and smart retail pedestrian retrieval centering on person re-identification (Re-ID) plays a pivotal role. Existing Re-ID methodologies often overlook subtle internal attribute variations which are crucial for accurately identifying individuals with changing appearances. In response our paper introduces the Attribute-Guided Pedestrian Retrieval (AGPR) task focusing on integrating specified attributes with query images to refine retrieval results. Although there has been progress in attribute-driven image retrieval there remains a notable gap in effectively blending robust Re-ID models with intra-class attribute variations. To bridge this gap we present the Attribute-Guided Transformer-based Pedestrian Retrieval (ATPR) framework. ATPR adeptly merges global ID recognition with local attribute learning ensuring a cohesive linkage between the two. Furthermore to effectively handle the complexity of attribute interconnectivity ATPR organizes attributes into distinct groups and applies both inter-group correlation and intra-group decorrelation regularizations. Our extensive experiments on a newly established benchmark using the RAP dataset demonstrate the effectiveness of ATPR within the AGPR paradigm. | [] | [] | [] | [] | 967 |
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968 | Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval | http://arxiv.org/abs/2403.17998 | Jiamian Wang, Guohao Sun, Pichao Wang, Dongfang Liu, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Zhiqiang Tao | 2,403.17998 | The increasing prevalence of video clips has sparked growing interest in text-video retrieval. Recent advances focus on establishing a joint embedding space for text and video relying on consistent embedding representations to compute similarity. However the text content in existing datasets is generally short and concise making it hard to fully describe the redundant semantics of a video. Correspondingly a single text embedding may be less expressive to capture the video embedding and empower the retrieval. In this study we propose a new stochastic text modeling method T-MASS i.e. text is modeled as a stochastic embedding to enrich text embedding with a flexible and resilient semantic range yielding a text mass. To be specific we introduce a similarity-aware radius module to adapt the scale of the text mass upon the given text-video pairs. Plus we design and develop a support text regularization to further control the text mass during the training. The inference pipeline is also tailored to fully exploit the text mass for accurate retrieval. Empirical evidence suggests that T-MASS not only effectively attracts relevant text-video pairs while distancing irrelevant ones but also enables the determination of precise text embeddings for relevant pairs. Our experimental results show a substantial improvement of T-MASS over baseline (3% 6.3% by R@1). Also T-MASS achieves state-of-the-art performance on five benchmark datasets including MSRVTT LSMDC DiDeMo VATEX and Charades. | [] | [] | [] | [] | 968 |
969 | Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning | Ziming Hong, Li Shen, Tongliang Liu | null | Recently non-transferable learning (NTL) was proposed to restrict models' generalization toward the target domain(s) which serves as state-of-the-art solutions for intellectual property (IP) protection. However the robustness of the established "transferability barrier" for degrading the target domain performance has not been well studied. In this paper we first show that the generalization performance of NTL models is widely impaired on third-party domains (i.e. the unseen domain in the NTL training stage). We explore the impairment patterns and find that: due to the dominant generalization of non-transferable task NTL models tend to make target-domain-consistent predictions on third-party domains even though only a slight distribution shift from the third-party domain to the source domain. Motivated by these findings we uncover the potential risks of NTL by proposing a simple but effective method (dubbed as TransNTL) to recover the target domain performance with few source domain data. Specifically by performing a group of different perturbations on the few source domain data we obtain diverse third-party domains that evoke the same impairment patterns as the unavailable target domain. Then we fine-tune the NTL model under an impairment-repair self-distillation framework where the source-domain predictions are used to teach the model itself how to predict on third-party domains thus repairing the impaired generalization. Empirically experiments on standard NTL benchmarks show that the proposed TransNTL reaches up to 72% target-domain improvements by using only 10% source domain data. Finally we also explore a feasible defense method and empirically demonstrate its effectiveness. | [] | [] | [] | [] | 969 |
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970 | Arbitrary Motion Style Transfer with Multi-condition Motion Latent Diffusion Model | Wenfeng Song, Xingliang Jin, Shuai Li, Chenglizhao Chen, Aimin Hao, Xia Hou, Ning Li, Hong Qin | null | Computer animation's quest to bridge content and style has historically been a challenging venture with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory motion content and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow guiding the overall structure and movement while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers setting a new benchmark in the realm of computer animation. The source code and model are available at https://github.com/XingliangJin/MCM-LDM.git. | [] | [] | [] | [] | 970 |
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971 | Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning | http://arxiv.org/abs/2404.03658 | Rui Li, Tobias Fischer, Mattia Segu, Marc Pollefeys, Luc Van Gool, Federico Tombari | 2,404.03658 | Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene representation limited to the image plane recent approaches based on radiance fields reconstruct a full 3D representation. However these methods still struggle with occluded regions since inferring geometry without visual observation requires (i) semantic knowledge of the surroundings and (ii) reasoning about spatial context. We propose KYN a novel method for single-view scene reconstruction that reasons about semantic and spatial context to predict each point's density. We introduce a vision-language modulation module to enrich point features with fine-grained semantic information. We aggregate point representations across the scene through a language-guided spatial attention mechanism to yield per-point density predictions aware of the 3D semantic context. We show that KYN improves 3D shape recovery compared to predicting density for each 3D point in isolation. We achieve state-of-the-art results in scene and object reconstruction on KITTI-360 and show improved zero-shot generalization compared to prior work. Project page: https://ruili3.github.io/kyn | [] | [] | [] | [] | 971 |
972 | Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction | http://arxiv.org/abs/2403.07346 | Jianping Jiang, Xinyu Zhou, Bingxuan Wang, Xiaoming Deng, Chao Xu, Boxin Shi | 2,403.07346 | Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties but it lacks key texture appearance for hand mesh reconstruction. In this paper we propose EvRGBHand -- the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time space and information dimensionsEvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity and background overflow issues in event-based HMR. We further propose EvRGBDegrader which allows our model to generalize effectively in challenging scenes even when trained solely on standard scenes thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both and shows the potential of generalization to outdoor scenes and another type of event camera. Our code models and dataset will be made public after acceptance. | [] | [] | [] | [] | 972 |
973 | Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance | Wei Yu, Jie Huang, Bing Li, Kaiwen Zheng, Qi Zhu, Man Zhou, Feng Zhao | null | Image enhancement algorithms have made remarkable advancements in recent years but directly applying them to Ultra-high-definition (UHD) images presents intractable computational overheads. Therefore previous straightforward solutions employ resampling techniques to reduce the resolution by adopting a "Downsampling-Enhancement-Upsampling" processing paradigm. However this paradigm disentangles the resampling operators and inner enhancement algorithms which results in the loss of information that is favored by the model further leading to sub-optimal outcomes. In this paper we propose a novel method of Learning Model-Aware Resampling (LMAR) which learns to customize resampling by extracting model-aware information from the UHD input image under the guidance of model knowledge. Specifically our method consists of two core designs namely compensatory kernel estimation and steganographic resampling. At the first stage we dynamically predict compensatory kernels tailored to the specific input and resampling scales. At the second stage the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images. This promotes the representation of the newly derived downscaled inputs to be more consistent with the full-resolution UHD inputs as perceived by the model. Our LMAR enables model-aware and model-favored resampling while maintaining compatibility with existing resampling operators. Extensive experiments on multiple UHD image enhancement datasets and different backbones have shown consistent performance gains after correlating resizer and enhancer e.g. up to 1.2dB PSNR gain for x1.8 resampling scale on UHD-LOL4K. The code is available at \href https://github.com/YPatrickW/LMAR https://github.com/YPatrickW/LMAR . | [] | [] | [] | [] | 973 |
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974 | ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions | Chunlong Xia, Xinliang Wang, Feng Lv, Xin Hao, Yifeng Shi | null | Although Vision Transformer (ViT) has achieved significant success in computer vision it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems which introduce additional pre-training costs. Therefore we present a plain pre-training-free and feature-enhanced ViT backbone with Convolutional Multi-scale feature interaction named ViT-CoMer which facilitates bidirectional interaction between CNN and transformer. Compared to the state-of-the-art ViT-CoMer has the following advantages: (1) We inject spatial pyramid multi-receptive field convolutional features into the ViT architecture which effectively alleviates the problems of limited local information interaction and single-feature representation in ViT. (2) We propose a simple and efficient CNN-Transformer bidirectional fusion interaction module that performs multi-scale fusion across hierarchical features which is beneficial for handling dense prediction tasks. (3) We evaluate the performance of ViT-CoMer across various dense prediction tasks different frameworks and multiple advanced pre-training. Notably our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data and 62.1% mIoU on ADE20K val both of which are comparable to state-of-the-art methods. We hope ViT-CoMer can serve as a new backbone for dense prediction tasks to facilitate future research. The code will be released at https://github.com/Traffic-X/ViT-CoMer. | [] | [] | [] | [] | 974 |
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975 | PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought | Junyi Yao, Yijiang Liu, Zhen Dong, Mingfei Guo, Helan Hu, Kurt Keutzer, Li Du, Daquan Zhou, Shanghang Zhang | null | Diffusion-based generative models have exhibited remarkable capability in the production of high-fidelity visual content such as images and videos. However their performance is significantly contingent upon the quality of textual inputs commonly referred to as "prompts". The process of traditional prompt engineering while effective necessitates empirical expertise and poses challenges for inexperienced users. In this paper we introduce PromptCoT an innovative enhancer that autonomously refines prompts for users. PromptCoT is designed based on the observation that prompts which resemble the textual information of high-quality images in the training set often lead to superior generation performance. Therefore we fine-tune the pre-trained Large Language Models (LLM) using a curated text dataset that solely comprises descriptions of high-quality visual content. By doing so the LLM can capture the distribution of high-quality training texts enabling it to generate aligned continuations and revisions to boost the original texts. Nonetheless one drawback of pre-trained LLMs is their tendency to generate extraneous or irrelevant information. We employ the Chain-of-Thought (CoT) mechanism to improve the alignment between the original text prompts and their refined versions. CoT can extract and amalgamate crucial information from the aligned continuation and revision enabling reasonable inferences based on the contextual cues to produce a more comprehensive and nuanced final output. Considering computational efficiency instead of allocating a dedicated LLM for prompt enhancement to each individual model or dataset we integrate adapters that facilitate dataset-specific adaptation leveraging a shared pre-trained LLM as the foundation for this process. With independent fine-tuning of these adapters we can adapt PromptCoT to new datasets while minimally increasing training costs and memory usage. We evaluate the effectiveness of PromptCoT by assessing its performance on widely-used latent diffusion models for image and video generation. The results demonstrate significant improvements in key performance metrics. | [] | [] | [] | [] | 975 |
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976 | Hallucination Augmented Contrastive Learning for Multimodal Large Language Model | http://arxiv.org/abs/2312.06968 | Chaoya Jiang, Haiyang Xu, Mengfan Dong, Jiaxing Chen, Wei Ye, Ming Yan, Qinghao Ye, Ji Zhang, Fei Huang, Shikun Zhang | 2,312.06968 | Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However MLLMs still face a fundamental limitation of hallucinations where they tend to generate erroneous or fabricated information. In this paper we address hallucinations in MLLMs from a novel perspective of representation learning. We first analyzed the representation distribution of textual and visual tokens in MLLM revealing two important findings: 1) there is a significant gap between textual and visual representations indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled making it challenging to distinguish them. These two observations inspire us with a simple yet effective method to mitigate hallucinations. Specifically we introduce contrastive learning into MLLMs and use text with hallucination as hard negative examples naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text. We evaluate our method quantitatively and qualitatively showing its effectiveness in reducing hallucination occurrences and improving performance across multiple benchmarks. On the MMhal-Bench benchmark our method obtains a 34.66% /29.5% improvement over the baseline MiniGPT-4/LLaVA. Our code is available on https://github.com/X-PLUG/mPLUG-HalOwl/tree/main/hacl. | [] | [] | [] | [] | 976 |
977 | Preserving Fairness Generalization in Deepfake Detection | http://arxiv.org/abs/2402.17229 | Li Lin, Xinan He, Yan Ju, Xin Wang, Feng Ding, Shu Hu | 2,402.17229 | Although effective deepfake detection models have been developed in recent years recent studies have revealed that these models can result in unfair performance disparities among demographic groups such as race and gender. This can lead to particular groups facing unfair targeting or exclusion from detection potentially allowing misclassified deepfakes to manipulate public opinion and undermine trust in the model. The existing method for addressing this problem is providing a fair loss function. It shows good fairness performance for intra-domain evaluation but does not maintain fairness for cross-domain testing. This highlights the significance of fairness generalization in the fight against deepfakes. In this work we propose the first method to address the fairness generalization problem in deepfake detection by simultaneously considering features loss and optimization aspects. Our method employs disentanglement learning to extract demographic and domain-agnostic forgery features fusing them to encourage fair learning across a flattened loss landscape. Extensive experiments on prominent deepfake datasets demonstrate our method's effectiveness surpassing state-of-the-art approaches in preserving fairness during cross-domain deepfake detection. The code is available at https://github.com/Purdue-M2/Fairness-Generalization. | [] | [] | [] | [] | 977 |
978 | Anomaly Score: Evaluating Generative Models and Individual Generated Images based on Complexity and Vulnerability | http://arxiv.org/abs/2312.10634 | Jaehui Hwang, Junghyuk Lee, Jong-Seok Lee | 2,312.10634 | With the advancement of generative models the assessment of generated images becomes increasingly more important. Previous methods measure distances between features of reference and generated images from trained vision models. In this paper we conduct an extensive investigation into the relationship between the representation space and input space around generated images. We first propose two measures related to the presence of unnatural elements within images: complexity which indicates how non-linear the representation space is and vulnerability which is related to how easily the extracted feature changes by adversarial input changes. Based on these we introduce a new metric to evaluating image-generative models called anomaly score (AS). Moreover we propose AS-i (anomaly score for individual images) that can effectively evaluate generated images individually. Experimental results demonstrate the validity of the proposed approach. | [] | [] | [] | [] | 978 |
979 | Structure-Aware Sparse-View X-ray 3D Reconstruction | Yuanhao Cai, Jiahao Wang, Alan Yuille, Zongwei Zhou, Angtian Wang | null | X-ray known for its ability to reveal internal structures of objects is expected to provide richer information for 3D reconstruction than visible light. Yet existing NeRF algorithms overlook this nature of X-ray leading to their limitations in capturing structural contents of imaged objects. In this paper we propose a framework Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF) for sparse-view X-ray 3D reconstruction. Firstly we design a Line Segment-based Transformer (Lineformer) as the backbone of SAX-NeRF. Linefomer captures internal structures of objects in 3D space by modeling the dependencies within each line segment of an X-ray. Secondly we present a Masked Local-Global (MLG) ray sampling strategy to extract contextual and geometric information in 2D projection. Plus we collect a larger-scale dataset X3D covering wider X-ray applications. Experiments on X3D show that SAX-NeRF surpasses previous NeRF-based methods by 12.56 and 2.49 dB on novel view synthesis and CT reconstruction. https://github.com/caiyuanhao1998/SAX-NeRF | [] | [] | [] | [] | 979 |
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980 | Dexterous Grasp Transformer | http://arxiv.org/abs/2404.18135 | Guo-Hao Xu, Yi-Lin Wei, Dian Zheng, Xiao-Ming Wu, Wei-Shi Zheng | 2,404.18135 | In this work we propose a novel discriminative framework for dexterous grasp generation named Dexterous Grasp TRansformer (DGTR) capable of predicting a diverse set of feasible grasp poses by processing the object point cloud with only one forward pass. We formulate dexterous grasp generation as a set prediction task and design a transformer-based grasping model for it. However we identify that this set prediction paradigm encounters several optimization challenges in the field of dexterous grasping and results in restricted performance. To address these issues we propose progressive strategies for both the training and testing phases. First the dynamic-static matching training (DSMT) strategy is presented to enhance the optimization stability during the training phase. Second we introduce the adversarial-balanced test-time adaptation (AB-TTA) with a pair of adversarial losses to improve grasping quality during the testing phase. Experimental results on the DexGraspNet dataset demonstrate the capability of DGTR to predict dexterous grasp poses with both high quality and diversity. Notably while keeping high quality the diversity of grasp poses predicted by DGTR significantly outperforms previous works in multiple metrics without any data pre-processing. Codes are available at https://github.com/iSEE-Laboratory/DGTR. | [] | [] | [] | [] | 980 |
981 | Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation | http://arxiv.org/abs/2312.06462 | Qi Yang, Xing Nie, Tong Li, Pengfei Gao, Ying Guo, Cheng Zhen, Pengfei Yan, Shiming Xiang | 2,312.06462 | Recently an audio-visual segmentation (AVS) task has been introduced aiming to group pixels with sounding objects within a given video. This task necessitates a first-ever audio-driven pixel-level understanding of the scene posing significant challenges. In this paper we propose an innovative audio-visual transformer framework termed COMBO an acronym for COoperation of Multi-order Bilateral relatiOns. For the first time our framework explores three types of bilateral entanglements within AVS: pixel entanglement modality entanglement and temporal entanglement. Regarding pixel entanglement we employ a Siam-Encoder Module (SEM) that leverages prior knowledge to generate more precise visual features from the foundational model. For modality entanglement we design a Bilateral-Fusion Module (BFM) enabling COMBO to align corresponding visual and auditory signals bi-directionally. As for temporal entanglement we introduce an innovative adaptive inter-frame consistency loss according to the inherent rules of temporal. Comprehensive experiments and ablation studies on AVSBench-object (84.7 mIoU on S4 59.2 mIou on MS3) and AVSBench-semantic (42.1 mIoU on AVSS) datasets demonstrate that COMBO surpasses previous state-of-the-art methods. Project page is available at https://yannqi.github.io/AVS-COMBO. | [] | [] | [] | [] | 981 |
982 | EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models | http://arxiv.org/abs/2311.15596 | Sijie Cheng, Zhicheng Guo, Jingwen Wu, Kechen Fang, Peng Li, Huaping Liu, Yang Liu | 2,311.15596 | Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities with the majority focusing on the third-person perspective and only a few addressing specific tasks from the first-person perspective. However the capability of VLMs to "think" from a first-person perspective a crucial attribute for advancing autonomous agents and robotics remains largely unexplored. To bridge this research gap we introduce EgoThink a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from egocentric videos with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs we evaluate twenty-one popular VLMs on EgoThink. Moreover given the open-ended format of the answers we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks. Meanwhile enlarging the number of trainable parameters has the most significant impact on model performance on EgoThink. In conclusion EgoThink serves as a valuable addition to existing evaluation benchmarks for VLMs providing an indispensable resource for future research in the realm of embodied artificial intelligence and robotics. | [] | [] | [] | [] | 982 |
983 | Hearing Anything Anywhere | Mason Long Wang, Ryosuke Sawata, Samuel Clarke, Ruohan Gao, Shangzhe Wu, Jiajun Wu | null | Recent years have seen immense progress in 3D computer vision and computer graphics with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However alongside immersive visual experiences immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene a setup that is easily achievable by ordinary users. To this end we introduce DiffRIR a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method we collect a dataset of RIR recordings and music in four diverse real environments. We show that our model outperforms state-of-the-art baselines on rendering monaural and binaural RIRs and music at unseen locations and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene. | [] | [] | [] | [] | 983 |
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984 | PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation | http://arxiv.org/abs/2312.02284 | Zhenyu Li, Shariq Farooq Bhat, Peter Wonka | 2,312.02284 | Single image depth estimation is a foundational task in computer vision and generative modeling. However prevailing depth estimation models grapple with accommodating the increasing resolutions commonplace in today's consumer cameras and devices. Existing high-resolution strategies show promise but they often face limitations ranging from error propagation to the loss of high-frequency details. We present PatchFusion a novel tile-based framework with three key components to improve the current state of the art: (1) A patch-wise fusion network that fuses a globally-consistent coarse prediction with finer inconsistent tiled predictions via high-level feature guidance (2) A Global-to-Local (G2L) module that adds vital context to the fusion network discarding the need for patch selection heuristics and (3) A Consistency-Aware Training (CAT) and Inference (CAI) approach emphasizing patch overlap consistency and thereby eradicating the necessity for post-processing. Experiments on UnrealStereo4K MVS-Synth and Middleburry 2014 demonstrate that our framework can generate high-resolution depth maps with intricate details. PatchFusion is independent of the base model for depth estimation. Notably our framework built on top of SOTA ZoeDepth brings improvements for a total of 17.3% and 29.4% in terms of the root mean squared error (RMSE) on UnrealStereo4K and MVS-Synth respectively. | [] | [] | [] | [] | 984 |
985 | GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image | http://arxiv.org/abs/2404.02152 | Chong Bao, Yinda Zhang, Yuan Li, Xiyu Zhang, Bangbang Yang, Hujun Bao, Marc Pollefeys, Guofeng Zhang, Zhaopeng Cui | 2,404.02152 | Recently we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars. However due to the diversity of frameworks there is no practical method to support high-level applications like 3D head avatar editing across different representations. In this paper we propose a generic avatar editing approach that can be universally applied to various 3DMM driving volumetric head avatars. To achieve this goal we design a novel expression-aware modification generative model which enables lift 2D editing from a single image to a consistent 3D modification field. To ensure the effectiveness of the generative modification process we develop several techniques including an expression-dependent modification distillation scheme to draw knowledge from the large-scale head avatar model and 2D facial texture editing tools implicit latent space guidance to enhance model convergence and a segmentation-based loss reweight strategy for fine-grained texture inversion. Extensive experiments demonstrate that our method delivers high-quality and consistent results across multiple expression and viewpoints. Project page: https://zju3dv.github.io/ geneavatar/. | [] | [] | [] | [] | 985 |
986 | Improved Self-Training for Test-Time Adaptation | Jing Ma | null | Test-time adaptation (TTA) is a technique to improve the performance of a pre-trained source model on a target distribution without using any labeled data. However existing self-trained TTA methods often face the challenges of unreliable pseudo-labels and unstable model optimization. In this paper we propose an Improved Self-Training (IST) approach which addresses these challenges by enhancing the pseudo-label quality and stabilizing the adaptation process. Specifically we use a simple augmentation strategy to generate multiple views of each test sample and construct a graph structure to correct the pseudo-labels based on the similarity of the latent features. Moreover we adopt a parameter moving average scheme to smooth the model updates and prevent catastrophic forgetting. Instead of using a model with fixed label space we explore the adaptability of the foundation model CLIP to various downstream tasks at test time. Extensive experiments on various benchmarks show that IST can achieve significant and consistent improvements over the existing TTA methods in classification detection and segmentation tasks. | [] | [] | [] | [] | 986 |
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987 | Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation | Jingyun Wang, Guoliang Kang | null | Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However we observe that when adopting CLIP to such a pixel-level understanding task unexpected bias occurs. Previous works don't explicitly model such bias which largely constrains the segmentation performance. In this paper we propose to explicitly model and rectify the bias existing in CLIP to facilitate the unsupervised semantic segmentation. Specifically we design a learnable "Reference" prompt to encode class-preference bias and project the positional embedding of vision transformer to represent space-preference bias. Via a simple element-wise subtraction we rectify the logits of CLIP classifier. Based on the rectified logits we generate a segmentation mask via a Gumbel-Softmax operation. Then a contrastive loss between masked visual feature and the text features of different classes is imposed to facilitate the effective bias modeling. To further improve the segmentation we distill the knowledge from the rectified CLIP to the advanced segmentation architecture via minimizing our designed mask-guided feature-guided and text-guided loss terms. Extensive experiments on standard benchmarks demonstrate that our method performs favorably against previous state-of-the-arts. The implementation is available at https://github.com/dogehhh/ReCLIP. | [] | [] | [] | [] | 987 |
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988 | Unsupervised Feature Learning with Emergent Data-Driven Prototypicality | http://arxiv.org/abs/2307.01421 | Yunhui Guo, Youren Zhang, Yubei Chen, Stella X. Yu | 2,307.01421 | Given a set of images our goal is to map each image to a point in a feature space such that not only point proximity indicates visual similarity but where it is located directly encodes how prototypical the image is according to the dataset. Our key insight is to perform unsupervised feature learning in hyperbolic instead of Euclidean space where the distance between points still reflects image similarity yet we gain additional capacity for representing prototypicality with the location of the point: The closer it is to the origin the more prototypical it is. The latter property is simply emergent from optimizing the metric learning objective: The image similar to many training instances is best placed at the center of corresponding points in Euclidean space but closer to the origin in hyperbolic space. We propose an unsupervised feature learning algorithm in Hyperbolic space with sphere pACKing. HACK first generates uniformly packed particles in the Poincar'e ball of hyperbolic space and then assigns each image uniquely to a particle. With our feature mapper simply trained to spread out training instances in hyperbolic space we observe that images move closer to the origin with congealing - a warping process that aligns all the images and makes them appear more common and similar to each other validating our idea of unsupervised prototypicality discovery. We demonstrate that our data-driven prototypicality provides an easy and superior unsupervised instance selection to reduce sample complexity increase model generalization with atypical instances and robustness with typical ones. | [] | [] | [] | [] | 988 |
989 | Unlocking Pre-trained Image Backbones for Semantic Image Synthesis | Tariq Berrada Ifriqi, Jakob Verbeek, Camille Couprie, Karteek Alahari | null | Semantic image synthesis i.e. generating images from user-provided semantic label maps is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation but the image quality tends to suffer when modeling large and diverse datasets. In this work we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbones pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables leading to more diverse generated images. Our model which we dub DP-SIMS achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K COCO-Stuff and Cityscapes surpassing recent diffusion models while requiring two orders of magnitude less compute for inference. | [] | [] | [] | [] | 989 |
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990 | Retrieval-Augmented Egocentric Video Captioning | http://arxiv.org/abs/2401.00789 | Jilan Xu, Yifei Huang, Junlin Hou, Guo Chen, Yuejie Zhang, Rui Feng, Weidi Xie | 2,401.00789 | Understanding human actions from videos of first-person view poses significant challenges. Most prior approaches explore representation learning on egocentric videos only while overlooking the potential benefit of exploiting existing large-scale third-person videos. In this paper (1) we develop EgoInstructor a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos (2) for training the cross-view retrieval module we devise an automatic pipeline to discover ego-exo video pairs from distinct large-scale egocentric and exocentric datasets (3) we train the cross-view retrieval module with a novel EgoExoNCE loss that pulls egocentric and exocentric video features closer by aligning them to shared text features that describe similar actions (4) through extensive experiments our cross-view retrieval module demonstrates superior performance across seven benchmarks. Regarding egocentric video captioning EgoInstructor exhibits significant improvements by leveraging third-person videos as references. | [] | [] | [] | [] | 990 |
991 | SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution | http://arxiv.org/abs/2312.11598 | Zhixuan Liang, Yao Mu, Hengbo Ma, Masayoshi Tomizuka, Mingyu Ding, Ping Luo | 2,312.11598 | Diffusion models have demonstrated strong potential for robotic trajectory planning. However generating coherent trajectories from high-level instructions remains challenging especially for long-range composition tasks requiring multiple sequential skills. We propose SkillDiffuser an end-to-end hierarchical planning framework integrating interpretable skill learning with conditional diffusion planning to address this problem. At the higher level the skill abstraction module learns discrete human-understandable skill representations from visual observations and language instructions. These learned skill embeddings are then used to condition the diffusion model to generate customized latent trajectories aligned with the skills. This allows generating diverse state trajectories that adhere to the learnable skills. By integrating skill learning with conditional trajectory generation SkillDiffuser produces coherent behavior following abstract instructions across diverse tasks. Experiments on multi-task robotic manipulation benchmarks like Meta-World and LOReL demonstrate state-of-the-art performance and human-interpretable skill representations from SkillDiffuser. More visualization results and information could be found on https://skilldiffuser.github.io/. | [] | [] | [] | [] | 991 |
992 | Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class Names | Yapeng Li, Yong Luo, Zengmao Wang, Bo Du | null | Generalized Zero-Shot Learning (GZSL) methods often assume that the unseen classes are similar to seen classes and thus perform poor when unseen classes are dissimilar to seen classes. Although some existing GZSL approaches can alleviate this issue by leveraging additional semantic information from test unseen classes their generalization ability to dissimilar unseen classes is still unsatisfactory. This motivates us to study GZSL in the more practical setting where unseen classes can be either similar or dissimilar to seen classes. In this paper we propose a simple yet effective GZSL framework by exploring diverse semantics from external class names (DSECN) which is simultaneously robust on the similar and dissimilar unseen classes. This is achieved by introducing diverse semantics from external class names and aligning the introduced semantics to visual space using the classification head of pre-trained network. Furthermore we show that the design idea of DSECN can easily be integrate into other advanced GZSL approaches such as the generative-based ones and enhance their robustness for dissimilar unseen classes. Extensive experiments in the practical setting including both similar and dissimilar unseen classes show that our method significantly outperforms the state-of-the-art approaches on all datasets and can be trained very efficiently. | [] | [] | [] | [] | 992 |
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993 | TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes | http://arxiv.org/abs/2312.04248 | Xuying Zhang, Bo-Wen Yin, Yuming Chen, Zheng Lin, Yunheng Li, Qibin Hou, Ming-Ming Cheng | 2,312.04248 | Recent progress in the text-driven 3D stylization of a single object has been considerably promoted by CLIP-based methods. However the stylization of multi-object 3D scenes is still impeded in that the image-text pairs used for pre-training CLIP mostly consist of an object. Meanwhile the local details of multiple objects may be susceptible to omission due to the existing supervision manner primarily relying on coarse-grained contrast of image-text pairs. To overcome these challenges we present a novel framework dubbed TeMO to parse multi-object 3D scenes and edit their styles under the contrast supervision at multiple levels. We first propose a Decoupled Graph Attention (DGA) module to distinguishably reinforce the features of 3D surface points. Particularly a cross-modal graph is constructed to align the object points accurately and noun phrases decoupled from the 3D mesh and textual description. Then we develop a Cross-Grained Contrast (CGC) supervision system where a fine-grained loss between the words in the textual description and the randomly rendered images are constructed to complement the coarse-grained loss. Extensive experiments show that our method can synthesize high-quality stylized content and outperform the existing methods over a wide range of multi-object 3D meshes. | [] | [] | [] | [] | 993 |
994 | TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression | Ho-Joong Kim, Jung-Ho Hong, Heejo Kong, Seong-Whan Lee | null | In this paper we investigate that the normalized coordinate expression is a key factor as reliance on hand-crafted components in query-based detectors for temporal action detection (TAD). Despite significant advancements towards an end-to-end framework in object detection query-based detectors have been limited in achieving full end-to-end modeling in TAD. To address this issue we propose TE-TAD a full end-to-end temporal action detection transformer that integrates time-aligned coordinate expression. We reformulate coordinate expression utilizing actual timeline values ensuring length-invariant representations from the extremely diverse video duration environment. Furthermore our proposed adaptive query selection dynamically adjusts the number of queries based on video length providing a suitable solution for varying video durations compared to a fixed query set. Our approach not only simplifies the TAD process by eliminating the need for hand-crafted components but also significantly improves the performance of query-based detectors. Our TE-TAD outperforms the previous query-based detectors and achieves competitive performance compared to state-of-the-art methods on popular benchmark datasets. Code is available at: https://github.com/Dotori-HJ/TE-TAD. | [] | [] | [] | [] | 994 |
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995 | GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding | http://arxiv.org/abs/2403.03608 | Zi-Ting Chou, Sheng-Yu Huang, I-Jieh Liu, Yu-Chiang Frank Wang | 2,403.03608 | Utilizing multi-view inputs to synthesize novel-view images Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work we introduce a Generalizable Semantic Neural Radiance Field (GSNeRF) which uniquely takes image semantics into the synthesis process so that both novel view images and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic Geo-Reasoning and Depth-Guided Visual rendering. The former is able to observe multi-view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified. | [] | [] | [] | [] | 995 |
996 | Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in Neural Radiance Fields | http://arxiv.org/abs/2404.02155 | Joshua Ahn, Haochen Wang, Raymond A. Yeh, Greg Shakhnarovich | 2,404.02155 | Scale-ambiguity in 3D scene dimensions leads to magnitude-ambiguity of volumetric densities in neural radiance fields i.e. the densities double when scene size is halved and vice versa. We call this property alpha invariance. For NeRFs to better maintain alpha invariance we recommend 1) parameterizing both distance and volume densities in log space and 2) a discretization-agnostic initialization strategy to guarantee high ray transmittance. We revisit a few popular radiance field models and find that these systems use various heuristics to deal with issues arising from scene scaling. We test their behaviors and show our recipe to be more robust. | [] | [] | [] | [] | 996 |
997 | TexTile: A Differentiable Metric for Texture Tileability | http://arxiv.org/abs/2403.12961 | Carlos Rodriguez-Pardo, Dan Casas, Elena Garces, Jorge Lopez-Moreno | 2,403.12961 | We introduce TexTile a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i.e. the tileability). Existing methods for tileable texture synthesis focus on general texture quality but lack explicit analysis of the intrinsic repeatability properties of a texture. In contrast our TexTile metric effectively evaluates the tileable properties of a texture opening the door to more informed synthesis and analysis of tileable textures. Under the hood TexTile is formulated as a binary classifier carefully built from a large dataset of textures of different styles semantics regularities and human annotations.Key to our method is a set of architectural modifications to baseline pre-train image classifiers to overcome their shortcomings at measuring tileability along with a custom data augmentation and training regime aimed at increasing robustness and accuracy. We demonstrate that TexTile can be plugged into different state-of-the-art texture synthesis methods including diffusion-based strategies and generate tileable textures while keeping or even improving the overall texture quality. Furthermore we show that TexTile can objectively evaluate any tileable texture synthesis method whereas the current mix of existing metrics produces uncorrelated scores which heavily hinders progress in the field. | [] | [] | [] | [] | 997 |
998 | D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection | http://arxiv.org/abs/2403.09359 | Dinh Phat Do, Taehoon Kim, Jaemin Na, Jiwon Kim, Keonho Lee, Kyunghwan Cho, Wonjun Hwang | 2,403.09359 | Domain adaptation for object detection typically entails transferring knowledge from one visible domain to another visible domain. However there are limited studies on adapting from the visible to the thermal domain because the domain gap between the visible and thermal domains is much larger than expected and traditional domain adaptation can not successfully facilitate learning in this situation. To overcome this challenge we propose a Distinctive Dual-Domain Teacher (D3T) framework that employs distinct training paradigms for each domain. Specifically we segregate the source and target training sets for building dual-teachers and successively deploy exponential moving average to the student model to individual teachers of each domain. The framework further incorporates a zigzag learning method between dual teachers facilitating a gradual transition from the visible to thermal domains during training. We validate the superiority of our method through newly designed experimental protocols with well-known thermal datasets i.e. FLIR and KAIST. Source code is available at https://github.com/EdwardDo69/D3T. | [] | [] | [] | [] | 998 |
999 | Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation | Lin Long, Haobo Wang, Zhijie Jiang, Lei Feng, Chang Yao, Gang Chen, Junbo Zhao | null | Positive-Unlabeled (PU) learning aims to train a binary classifier using minimal positive data supplemented by a substantially larger pool of unlabeled data in the specific absence of explicitly annotated negatives. Despite its straightforward nature as a binary classification task the currently best-performing PU algorithms still largely lag behind the supervised counterpart. In this work we identify that the primary bottleneck lies in the difficulty of deriving discriminative representations under unreliable binary supervision with poor semantics which subsequently hinders the common label disambiguation procedures. To cope with this problem we propose a novel PU learning framework namely Latent Group-Aware Meta Disambiguation (LaGAM) which incorporates a hierarchical contrastive learning module to extract the underlying grouping semantics within PU data and produce compact representations. As a result LaGAM enables a more aggressive label disambiguation strategy where we enhance the robustness of training by iteratively distilling the true labels of unlabeled data directly through meta-learning. Extensive experiments show that LaGAM significantly outperforms the current state-of-the-art methods by an average of 6.8% accuracy on common benchmarks approaching the supervised baseline. We also provide comprehensive ablations as well as visualized analysis to verify the effectiveness of our LaGAM. | [] | [] | [] | [] | 999 |
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