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arXiv · 2606.01097

Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R

Yuyang Sun·Yongliang Wu·Xingyu Zhu·Yuxia Chen·Zhenxiang Jiang·Yangguang Ji·Wenbo Zhu·Yanxi Shi·Jay Wu·Shuo Wang·Xu Yang
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Abstract

We describe \emph{Dual-Route Top-K Retrieval with 1v1 VLM Reranking} for the CoVR-R challenge. The method treats composed video retrieval as two coupled problems: finding a sufficiently complete top-k candidate set, and then safely deciding whether any candidate should replace a strong current top-1. We first improve the reasoning/text seed with a VLM slot selector over existing candidates, without introducing DFN visual retrieval. We then add a visual route from contact-sheet embeddings using DFN-H/DFN-L. The routes are merged into a top-10 candidate set, after which a VLM final reranker performs conservative 1v1 comparisons between the current top-1 and each challenger. On the hidden test split, the final system reaches 95.28 R@1, 97.47 R@5, 98.48 R@10, and 99.66 R@50. The main lesson is that CoVR-R benefits more from recall-selection decoupling than from broad text reranking or direct multi-candidate VLM classification.

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Yuyang Sun, Yongliang Wu, Xingyu Zhu, Yuxia Chen, Zhenxiang Jiang, Yangguang Ji, Wenbo Zhu, Yanxi Shi, Jay Wu, Shuo Wang, Xu Yang. 2026-05-31. Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R. https://arxiv.org/abs/2606.01097

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