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VEBench benchmark evaluates large multimodal models for video editing tasks

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have introduced VEBENCH, a new benchmark designed to evaluate Large Multimodal Models (LMMs) in real-world video editing tasks. The benchmark includes over 3.9K edited videos and 3,080 question-answer pairs, focusing on recognizing editing techniques and simulating editing workflows. Experiments using VEBENCH revealed a significant performance gap between current LMMs and human capabilities in video editing, highlighting the need for improved multimodal reasoning and operational capabilities. AI

影响 Establishes a new standard for evaluating AI in video editing, potentially guiding future development of more capable creative AI tools.

排序理由 This is a research paper introducing a new benchmark for evaluating AI models.

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

VEBench benchmark evaluates large multimodal models for video editing tasks

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Andong Deng, Dawei Du, Zhenfang Chen, Wen Zhong, Fan Chen, Guang Chen, Chia-Wen Kuo, Longyin Wen, Chen Chen, Sijie Zhu ·

    VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing

    arXiv:2605.03276v1 Announce Type: new Abstract: Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. While recent Large Multimodal Models (LMMs) have shown …

  2. arXiv cs.CV TIER_1 English(EN) · Sijie Zhu ·

    VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing

    Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. While recent Large Multimodal Models (LMMs) have shown remarkable progress in general video understandi…

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