Test-Time Adaptation for Text-Based Person Search

Text-based person search (TBPS), aiming to retrieve target pedestrian images with natural language descriptions, has seen significant progress in recent years. However, severe domain shift remains a key challenge in this field, causing source-domain-trained models to degrade significantly when applied to an unseen target domain. To address this, we propose the Identity-preserving Cross-modal Alignment and Adaptation (ICAA) model, a novel test-time adaptation framework for TBPS that enables seamless domain adaptation using only unlabeled target samples. Our method tackles two key challenges: 1) Cross-modal domain-shift misalignment: textual and visual modalities exhibit inconsistent distributional shifts across domains. To this end, our Cross-Modal Alignment adaptation (CMA) module identifies pseudo-positive image-text pairs and minimizes their matching discrepancies in the target domain, adapting to new cross-modal distribution relationships. 2) Identity semantic absence: crucial identity annotations are usually unavailable in both target text and image data. To mitigate this, we introduce the Identity-Preserving Dynamic adaptation (IPD) module, which dynamically associates image-text pairs with potential identity prototypes to enhance identity consistency in cross-modal alignment during adaptation. Our method is simple yet effective, establishing new state-of-the-art cross-domain results for TBPS on three public benchmarks, i.e., CUHK-PEDES, ICFG-PEDES, and RSTPReid.