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English(EN) CodeCap-Bench: Can Code Serve as a Good Video Caption?

代码字幕在视频分析方面显示出潜力，但MLLM在处理复杂性方面遇到困难

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新的研究论文介绍CodeCap-Bench，这是一个旨在评估多模态大语言模型（MLLM）为视频生成代码字幕能力的基准。研究发现，尽管与自然语言字幕相比，代码字幕提供了更强的推理和重建能力，但目前的MLLM在处理复杂的时间动态方面仍面临挑战。领先的专有模型显示出潜力，但开源模型在此领域明显落后。 AI

影响 强调了一种新的MLLM评估方法，指出了视频理解和代码生成方面的改进领域。

排序理由 介绍用于评估多模态模型的新基准的研究论文。

在 Hugging Face Daily Papers 阅读 →

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代码字幕在视频分析方面显示出潜力，但MLLM在处理复杂性方面遇到困难

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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    CodeCap-Bench：代码能否成为良好的视频字幕？

    Executable code representations of video content improve reasoning and reconstruction over natural language, yet current multimodal models still struggle to generate faithful code captions for complex temporal dynamics.

  2. arXiv cs.CV TIER_1 English(EN) · Jiafu Tang, Yifan Xu, Eric Tang, Junjie Zhao, Shipeng Luo, Te Sun, Haoxuan Hu, Yiyan Ji, Xinlong Chen, Zihao Wang, Yang Shi, Yuanxing Zhang, Pengfei Wan, Jiaheng Liu ·

    CodeCap-Bench：代码能否成为良好的视频字幕？

    arXiv:2610.37341v1 Announce Type: new Abstract: Video captioning aims to preserve visual information through compact descriptions, predominantly using natural language. However, precisely expressing fine-grained spatial structures and temporal dynamics in natural language often l…

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