Concentrate After Imagination: Text-Conditioned Evidence Grounding for Partially Relevant Video Retrieval

2026-09-08

AI Summary

为解决部分相关视频检索中的查询无关集中瓶颈问题，提出TRACE方法，通过基于查询的证据验证来校准局部分数，提高检索准确性。

Abstract

Partially Relevant Video Retrieval (PRVR) retrieves untrimmed videos when queries describe only short moments. Although recent methods improve local representations, uncertainty modeling, and global context, final ranking often still trusts the strongest local response; a coincidentally similar fragment can therefore produce an unsupported peak. We identify this failure as the query-agnostic concentration bottleneck and propose TRACE, a score-level evidence verification operator for PRVR. Given a query and global video registers, TRACE activates query-relevant registers, routes their support to frame-level evidence, and smoothly marginalizes alternative query-to-register-to-frame paths before localized temporal selection. Unlike representation-level feature fusion, TRACE uses this evidence only as a query-conditioned residual calibration of the original local score. On ActivityNet Captions, Charades-STA, and TVR, TRACE achieves the best SumR on all three benchmarks and improves the DreamPRVR backbone by 1.2, 1.1, and 1.5 points, respectively. Ablation, routing-corruption, hard-negative, and cross-backbone transfer analyses support the interpretation that the gains arise from query-conditioned evidence verification rather than a generic score offset.

Problem

• Partially Relevant Video Retrieval
• local response
• query-agnostic concentration bottleneck

Innovation

• TRACE
• score-level evidence verification
• query-conditioned residual calibration
• partially relevant video retrieval (PRVR)