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New ShotFinder benchmark reveals multimodal LLMs struggle with video editing

Researchers have introduced ShotFinder, a new benchmark designed to evaluate open-domain video shot retrieval capabilities of large language models. The benchmark formalizes editing requirements into keyframe-oriented shot descriptions with five types of controllable constraints: temporal order, color, visual style, audio, and resolution. A dataset of 1,210 samples was curated from YouTube, and a three-stage retrieval and localization pipeline was proposed. Experiments indicate a significant performance gap between current models and human capabilities, with color and visual style posing the greatest challenges for multimodal large models. AI

IMPACT Highlights current limitations of multimodal LLMs in complex video editing tasks, indicating areas for future research and development.

RANK_REASON The item describes a new benchmark and associated paper for evaluating AI capabilities in video shot retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New ShotFinder benchmark reveals multimodal LLMs struggle with video editing

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The item describes a new benchmark and associated paper for evaluating AI capabilities in video shot retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tao Yu, Haopeng Jin, Hao Wang, Shenghua Chai, Yujia Yang, Junhao Gong, Jiaming Guo, Minghui Zhang, Xinlong Chen, Zhenghao Zhang, Yuxuan Zhou, Yufei Xiong, Shanbin Zhang, Jiabing Yang, YiFan Zhang, Hongzhu Yi, Xinming Wang, Cheng Zhong, Xiao Ma, Zhang Zha… ·

    ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search

    arXiv:2601.23232v4 Announce Type: replace-cross Abstract: In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Open-domain video shot retrieval, which invol…