Text-Video Retrieval (TVR) methods typically match query-candidate pairs by aligning text and video features in coarse-grained, fine-grained, or combined (coarse-to-fine) manners. However, these frameworks predominantly employ a one(query)-to-one(candidate) alignment paradigm, which struggles to discern nuanced differences among candidates, leading to frequent mismatches. Inspired by Comparative Judgement in human cognitive science, where decisions are made by directly comparing items rather than evaluating them independently, we propose TokenBinder. This innovative two-stage TVR framework introduces a novel one-to-many coarse-to-fine alignment paradigm, imitating the human cognitive process of identifying specific items within a large collection. Our method employs a Focused-view Fusion Network with a sophisticated cross-attention mechanism, dynamically aligning and comparing features across multiple videos to capture finer nuances and contextual variations. Extensive experiments on six benchmark datasets confirm that TokenBinder substantially outperforms existing state-of-the-art methods. These results demonstrate its robustness and the effectiveness of its fine-grained alignment in bridging intra- and inter-modality information gaps in TVR tasks.

本研究解决了文本视频检索中常见的一对一对齐方法难以识别候选视频间细微差异的问题。提出的TokenBinder框架采用创新的二阶段一对多对齐方法，通过焦点融合网络动态对齐和比较多视频特征，从而捕捉更微妙的语境变化。实验证明，该方法在多个基准数据集上显著优于现有的最佳方法，展现了其在弥补信息差距方面的有效性。

TokenBinder：基于一对多对齐范式的文本视频检索