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@@ -28,8 +28,26 @@ For further details, please refer to the following resources:
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  - πŸ‘‹ Point of Contact: [Yibin Wang](https://codegoat24.github.io)
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  ### Quick Start
 
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  ~~~python
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  # pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
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  from llava.model.builder import load_pretrained_model
 
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  - πŸ‘‹ Point of Contact: [Yibin Wang](https://codegoat24.github.io)
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+ ## 🏁 Compared with Current Reward Models
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+ | Reward Model | Method| Image Generation | Image Understanding | Video Generation | Video Understanding
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+ | :-----: | :-----: |:-----: |:-----: | :-----: | :-----: |
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+ | [PickScore](https://github.com/yuvalkirstain/PickScore) |Point | √ | | ||
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+ | [HPS](https://github.com/tgxs002/HPSv2) | Point | √ | |||
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+ | [ImageReward](https://github.com/THUDM/ImageReward) | Point| √| |||
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+ | [LLaVA-Critic](https://huggingface.co/lmms-lab/llava-critic-7b) | Pair/Point | | √ |||
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+ | [IXC-2.5-Reward](https://github.com/InternLM/InternLM-XComposer) | Pair/Point | | √ ||√|
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+ | [VideoScore](https://github.com/TIGER-AI-Lab/VideoScore) | Point | | |√ ||
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+ | [LiFT](https://github.com/CodeGoat24/LiFT) | Point | | |√| |
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+ | [VisionReward](https://github.com/THUDM/VisionReward) | Point |√ | |√||
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+ | [VideoReward](https://github.com/KwaiVGI/VideoAlign) | Point | | |√ ||
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+ | UnifiedReward (Ours) | Pair/Point | √ | √ |√|√|
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  ### Quick Start
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+ All pair rank and point score inference codes are provided in our [github](https://github.com/CodeGoat24/UnifiedReward).
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+ We take image understanding assessment as example here:
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  ~~~python
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  # pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
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  from llava.model.builder import load_pretrained_model