MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding
The dominant paradigm in video retrieval relies on embedding-based full-corpus scanning, which suffers from inherent computational inefficiency and the semantic asymmetry between information-dense videos and sparse textual queries. To bridge this gap, we introduce \textbf{MAVIS}, a novel multi-agent framework that rethinks retrieval as cooperative reasoning rather than brute-force search. MAVIS first bridges the granularity mismatch by parsing raw videos into a \textbf{Structured Semantic Library}, enabling explicit attribute-level indexing. During retrieval, a planner decomposes complex user intents into atomic sub-tasks, dispatching specialized agents to independently nominate candidate...
| Authors | Jie Zhang, Qilang Ye, Hao Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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