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- # πŸ‘©β€βš–οΈ [**MJ-Bench**: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?](https://mj-bench.github.io/)
 
 
 
 
 
 
 
 
 
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  Project page: https://mj-bench.github.io/
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  Code repository: https://github.com/MJ-Bench/MJ-Bench
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  To address this issue, we introduce MJ-Bench, a novel benchmark which incorporates a comprehensive preference dataset to evaluate multimodal judges in providing feedback for image generation models across four key perspectives: **alignment**, **safety**, **image quality**, and **bias**.
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- ![Dataset Overview](https://raw.githubusercontent.com/MJ-Bench/MJ-Bench.github.io/main/static/images/dataset_overview.png)
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  Specifically, we evaluate a large variety of multimodal judges including
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  - 11 open-source VLMs (e.g. LLaVA family)
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  - 4 and close-source VLMs (e.g. GPT-4o, Claude 3)
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- ![Radar Plot](https://raw.githubusercontent.com/MJ-Bench/MJ-Bench.github.io/main/static/images/radar_plot.png)
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  πŸ”₯πŸ”₯We are actively updating the [leaderboard](https://mj-bench.github.io/) and you are welcome to submit the evaluation result of your multimodal judge on [our dataset](https://huggingface.co/datasets/MJ-Bench/MJ-Bench) to [huggingface leaderboard](https://huggingface.co/spaces/MJ-Bench/MJ-Bench-Leaderboard).
 
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+ # MJ-Bench Team: Align
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+ ## 😎 [**MJ-Video**: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation](https://aiming-lab.github.io/MJ-VIDEO.github.io/)
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+ We release MJ-Bench-Video, a comprehensive fine-grained video preference benchmark, and MJ-Video, a powerful MoE-based multi-dimensional video reward model!
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+ ## πŸ‘©β€βš–οΈ [**MJ-Bench**: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?](https://mj-bench.github.io/)
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  Project page: https://mj-bench.github.io/
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  Code repository: https://github.com/MJ-Bench/MJ-Bench
 
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  To address this issue, we introduce MJ-Bench, a novel benchmark which incorporates a comprehensive preference dataset to evaluate multimodal judges in providing feedback for image generation models across four key perspectives: **alignment**, **safety**, **image quality**, and **bias**.
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+ <!-- ![Dataset Overview](https://raw.githubusercontent.com/MJ-Bench/MJ-Bench.github.io/main/static/images/dataset_overview.png) -->
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  Specifically, we evaluate a large variety of multimodal judges including
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  - 11 open-source VLMs (e.g. LLaVA family)
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  - 4 and close-source VLMs (e.g. GPT-4o, Claude 3)
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  -
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+ <!-- ![Radar Plot](https://raw.githubusercontent.com/MJ-Bench/MJ-Bench.github.io/main/static/images/radar_plot.png) -->
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  πŸ”₯πŸ”₯We are actively updating the [leaderboard](https://mj-bench.github.io/) and you are welcome to submit the evaluation result of your multimodal judge on [our dataset](https://huggingface.co/datasets/MJ-Bench/MJ-Bench) to [huggingface leaderboard](https://huggingface.co/spaces/MJ-Bench/MJ-Bench-Leaderboard).