RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval

• URL: http://arxiv.org/abs/2602.02444v2
• Date: Tue, 03 Feb 2026 16:18:39 GMT
• Title: RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval
• Authors: Tyler Skow, Alexander Martin, Benjamin Van Durme, Rama Chellappa, Reno Kriz,
• Abstract summary: We introduce RANKVIDEO, a reasoning-based reranker for video retrieval.<n>RANKVIDEO explicitly reasons over query-video pairs using video content to assess relevance.<n> Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework.
• Score: 99.33724613432922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reranking is a critical component of modern retrieval systems, which typically pair an efficient first-stage retriever with a more expressive model to refine results. While large reasoning models have driven rapid progress in text-centric reranking, reasoning-based reranking for video retrieval remains underexplored. To address this gap, we introduce RANKVIDEO, a reasoning-based reranker for video retrieval that explicitly reasons over query-video pairs using video content to assess relevance. RANKVIDEO is trained using a two-stage curriculum consisting of perception-grounded supervised fine-tuning followed by reranking training that combines pointwise, pairwise, and teacher confidence distillation objectives, and is supported by a data synthesis pipeline for constructing reasoning-intensive query-video pairs. Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework, yielding an average improvement of 31% on nDCG@10 and outperforming text-only and vision-language reranking alternatives, while more efficient.

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