Identity Semantic Correspondence for Cloth-Changing Person Re-Identification

Cloth-changing Person Re-Identification (CC-ReID) aims at retrieving the same person who might change clothes across different locations. Although remarkable progress has been achieved in recent studies, most of the recent methods still lack sufficient emphasis on identity-related regions. To address these issues, we propose a novel Identity Semantic Correspondence framework (ISC) to fully utilize human semantic information, which includes dual-stream identity semantic correspondance networks, i.e., a Clothing-invariant Identity (CI) stream and a Fine-grained Identity Semantic Highlighting (FISH) stream. The CI mitigates the interference of human dressing and enhances clothing-invariant identity information by erasing clothing information. And, the FISH exploits fine-grained identity information by highlighting contribution of different body parts to identity with a part-aware weighting module. Additionally, an identity semantic consistency module is further proposed to extract the most representative and discriminative semantic features for each identity. Besides, we employ a mutual loss to transfer identity-related knowledge between components, which enables the original appearance module to be deployed independently during the inference stage. Extensive experiments on two CC-ReID benchmarks, including PRCC and VC-Clothes, are conducted to demonstrate the effectiveness of the proposed ISC method.