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update gh-pages
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index.html

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</div>
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<div style="text-align: center;">
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<img id="teaser" width="85%" src="static/images/teaser.png">
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<img id="teaser" width="80%" src="static/images/teaser.png">
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<section class="section">
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We test VideoScore2 on the test set of VideoFeedback2, containing 500 videos with human scores from three dimensions.
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Below we show the results of some scoring/reward models for image or video, like VideoReward, VisionReward, Q-Insight, DeQA-Score, and MLLM-prompting methods like Claude-Sonnet-4, GPT-5, Gemini-2.5-Pro, etc and our VideoScore2.
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</p>
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<img id="res_vs2_bench" width="70%" src="static/images/res_vs2_bench.png">
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<centering>
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<div style="text-align: center;">
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<img id="res_vs2_bench" width="70%" src="static/images/res_vs2_bench.png">
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</centering>
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Preference benchmark results include ties. As shown in Table below, while VideoScore2 is
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not always the top model on each benchmark, it achieves the highest overall average.
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</p>
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<img id="res_ood_bench" width="70%" src="static/images/res_ood_bench.png">
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<centering>
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<div style="text-align: center;">
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<img id="res_ood_bench" width="70%" src="static/images/res_ood_bench.png">
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</centering>
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Comparison on VBench shows BoN consistently outperforms random sampling, confirm
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ing that VIDEOSCORE2 effectively guides higher-quality selection.
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</p>
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<img id="BoN" width="70%" src="static/images/BoN.png">
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<centering>
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<div style="text-align: center;">
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<img id="BoN" width="70%" src="static/images/BoN.png">
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</centering>
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</h2>
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</div>
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</section>
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<img id="cs1" width="70%" src="static/images/case1.png">
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<img id="cs2" width="70%" src="static/images/case2.png">
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<img id="cs3" width="70%" src="static/images/case3.png">
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<centering>
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<div style="text-align: center;">
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<img id="cs1" width="50%" src="static/images/case1.png">
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<img id="cs2" width="50%" src="static/images/case2.png">
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<img id="cs3" width="50%" src="static/images/case3.png">
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</div>
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</centering>
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<section class="section" id="BibTeX">
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<div class="container is-max-desktop content">

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