SPCL: Semantic Polymorphism and Commonality Learning for Text-based Person Retrieval

Text-Based Person Retrieval (TBPR) refers to identifying a specific target pedestrian image based on natural language descriptions. Most previous methods rely on one-to-one alignment between paired text-image data, ignoring the polymorphic nature of visual and linguistic information. Moreover, constrained by ID, earlier methods have shown limited exploration of intra-individual and inter-individual relations. This limitation confines them to exploring characteristics within individuals, making it challenging to uncover commonalities and invariants that extend across IDs (e.g., attributes). Recently, due to the lack of accurate annotations, exploring attribute-based cross-modal interactions and alignments has become a significant challenge in TBPR. To address these issues, we propose a Semantic Polymorphism and Commonality Learning (SPCL) framework. First, we present Relation-Sensitive Semantic Polymorphism Alignment (RSSPA) and ID-Based Semantic Polymorphism Alignment (IBSPA) to explore ID-limited Feature Redistribution. Second, we transcend the constraints of ID, leveraging ID-Free Attribute Alignment (IFAA) from a macro perspective to explore commonalities and invariants based on attribute features. Finally, from a micro perspective, we design Attribute Prior Fusion Reconstruction (APFR) to optimize the attention of our model, exploring the positive impact of attribute priors on cross-modal interaction. Experiments on CUHK-PEDES, ICFG-PEDES and RSTPReid show that our method achieves state-of-the-art performance on Rank-1, mAP and mINP.