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arXiv · 2606.02321

Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning

Yang Liu·Qianqian Xu·Peisong Wen·Siran Dai·Qingming Huang
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Abstract

Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may specify the desired result through both visual examples and textual instructions. In the CVPR 2026 Reason-Aware Composed Video Retrieval Challenge, the system is required to retrieve a target video according to a reference video and a modification instruction. To address this task, we develop Visual Representation-Guided Video-LLM Reasoning for Training-Free Composed Video Retrieval. Our framework first uses frozen DINOv3 models to obtain a compact set of visually relevant candidates, and then applies large vision-language models to evaluate whether each candidate satisfies the modification instruction. A final reasoning-based refinement is further performed on the top candidates to improve the first-ranked prediction. Without training, our system achieves 48.78 Recall@1 and 51.48 Recall@5 on the test set. Future work may further improve retrieval accuracy through stronger video-LLMs and detailed integration between visual representations and language reasoning.

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Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai, Qingming Huang. 2026-06-01. Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning. https://arxiv.org/abs/2606.02321

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