Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917266301386752 |
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| author | Yu, Tao Yang, Yujia Jin, Haopeng Gong, Junhao Chen, Xinlong Zhou, Yuxuan Zhang, Shanbin Yang, Jiabing Wang, Xinming Yi, Hongzhu Nie, Ping Zou, Kai Zhang, Zhang Huang, Yan Wang, Liang Yeshani Tao, Ruiwen Ma, Jin Liang, Haijin Luo, Jinwen |
| author_facet | Yu, Tao Yang, Yujia Jin, Haopeng Gong, Junhao Chen, Xinlong Zhou, Yuxuan Zhang, Shanbin Yang, Jiabing Wang, Xinming Yi, Hongzhu Nie, Ping Zou, Kai Zhang, Zhang Huang, Yan Wang, Liang Yeshani Tao, Ruiwen Ma, Jin Liang, Haijin Luo, Jinwen |
| contents | Traditional video retrieval benchmarks focus on matching precise descriptions to closed video pools, failing to reflect real-world searches characterized by fuzzy, multi-dimensional memories on the open web. We present \textbf{RVMS-Bench}, a comprehensive system for evaluating real-world video memory search. It consists of \textbf{1,440 samples} spanning \textbf{20 diverse categories} and \textbf{four duration groups}, sourced from \textbf{real-world open-web videos}. RVMS-Bench utilizes a hierarchical description framework encompassing \textbf{Global Impression, Key Moment, Temporal Context, and Auditory Memory} to mimic realistic multi-dimensional search cues, with all samples strictly verified via a human-in-the-loop protocol. We further propose \textbf{RACLO}, an agentic framework that employs abductive reasoning to simulate the human ``Recall-Search-Verify'' cognitive process, effectively addressing the challenge of searching for videos via fuzzy memories in the real world. Experiments reveal that existing MLLMs still demonstrate insufficient capabilities in real-world Video Retrieval and Moment Localization based on fuzzy memories. We believe this work will facilitate the advancement of video retrieval robustness in real-world unstructured scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10159 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization Yu, Tao Yang, Yujia Jin, Haopeng Gong, Junhao Chen, Xinlong Zhou, Yuxuan Zhang, Shanbin Yang, Jiabing Wang, Xinming Yi, Hongzhu Nie, Ping Zou, Kai Zhang, Zhang Huang, Yan Wang, Liang Yeshani Tao, Ruiwen Ma, Jin Liang, Haijin Luo, Jinwen Computer Vision and Pattern Recognition Machine Learning Traditional video retrieval benchmarks focus on matching precise descriptions to closed video pools, failing to reflect real-world searches characterized by fuzzy, multi-dimensional memories on the open web. We present \textbf{RVMS-Bench}, a comprehensive system for evaluating real-world video memory search. It consists of \textbf{1,440 samples} spanning \textbf{20 diverse categories} and \textbf{four duration groups}, sourced from \textbf{real-world open-web videos}. RVMS-Bench utilizes a hierarchical description framework encompassing \textbf{Global Impression, Key Moment, Temporal Context, and Auditory Memory} to mimic realistic multi-dimensional search cues, with all samples strictly verified via a human-in-the-loop protocol. We further propose \textbf{RACLO}, an agentic framework that employs abductive reasoning to simulate the human ``Recall-Search-Verify'' cognitive process, effectively addressing the challenge of searching for videos via fuzzy memories in the real world. Experiments reveal that existing MLLMs still demonstrate insufficient capabilities in real-world Video Retrieval and Moment Localization based on fuzzy memories. We believe this work will facilitate the advancement of video retrieval robustness in real-world unstructured scenarios. |
| title | Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2602.10159 |