Add paper and code links to dataset card
Browse filesThis PR enhances the dataset card for VidComposition by adding direct links to the paper ([https://huggingface.co/papers/2411.10979](https://huggingface.co/papers/2411.10979)) and the associated GitHub repository ([https://github.com/yunlong10/VidComposition](https://github.com/yunlong10/VidComposition)) at the top of the README for easier access to the research and code.
README.md
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- multiple-choice
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# VidComposition Benchmark
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[🖥 Project Page](https://yunlong10.github.io/VidComposition) | [🚀 Evaluation Space](https://huggingface.co/spaces/JunJiaGuo/VidComposition)
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The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability to understand video compositions, the nuanced interpretation of how visual elements combine and interact within highly compiled video contexts. We introduce **VidComposition**, a new benchmark specifically designed to evaluate the video composition understanding capabilities of MLLMs using carefully curated compiled videos and cinematic-level annotations. VidComposition includes 982 videos with 1706 multiple-choice questions, covering various compositional aspects such as camera movement, angle, shot size, narrative structure, character actions and emotions, etc. Our comprehensive evaluation of 33 open-source and proprietary MLLMs reveals a significant performance gap between human and model capabilities. This highlights the limitations of current MLLMs in understanding complex, compiled video compositions and offers insights into areas for further improvement.
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- multiple-choice
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- video-text-to-text
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# VidComposition Benchmark
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[\📄 Paper](https://huggingface.co/papers/2411.10979) | [\💻 Code](https://github.com/yunlong10/VidComposition) | [🖥 Project Page](https://yunlong10.github.io/VidComposition) | [🚀 Evaluation Space](https://huggingface.co/spaces/JunJiaGuo/VidComposition)
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The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability to understand video compositions, the nuanced interpretation of how visual elements combine and interact within highly compiled video contexts. We introduce **VidComposition**, a new benchmark specifically designed to evaluate the video composition understanding capabilities of MLLMs using carefully curated compiled videos and cinematic-level annotations. VidComposition includes 982 videos with 1706 multiple-choice questions, covering various compositional aspects such as camera movement, angle, shot size, narrative structure, character actions and emotions, etc. Our comprehensive evaluation of 33 open-source and proprietary MLLMs reveals a significant performance gap between human and model capabilities. This highlights the limitations of current MLLMs in understanding complex, compiled video compositions and offers insights into areas for further improvement.
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