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]
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