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arXiv · 2606.28326

ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval

Ke Chen·Shengyuan Han·Yongfeng Huang·Yujin Zhu·Jingwei Xiong·Liang Xu·Jundong Liu
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Abstract

This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries. Prevailing single-round retrieval paradigms face a performance bottleneck, as they lack effective feedback mechanisms to handle complex search intentions. The root cause is the "Intent-Query Gap", where users' intent cannot be captured by a simple text query. To solve this, we propose the ADEPT framework: a training-free agent that pioneers an entropy-driven decision engine to efficiently guide dialogue by dynamically selecting between ASK and REFINE strategies. Experiments on two challenging datasets demonstrate that ADEPT significantly outperforms all non-interactive, heuristic, and Video-LLM baselines. The core contribution of this work is an efficient and interpretable entropy-driven interactive strategy that sets a new performance benchmark for the field of interactive video retrieval.

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Ke Chen, Shengyuan Han, Yongfeng Huang, Yujin Zhu, Jingwei Xiong, Liang Xu, Jundong Liu. 2026-05-07. ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval. https://doi.org/10.1109/icassp55912.2026.11465079

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