AIDA-ReID: Adaptive Intermediate Domain Adaptation for Generalizable and Source-Free Person Re-Identification

Person re-identification (Re-ID) is challenging due to domain shifts. This paper proposes Adaptive Intermediate Domain Adaptation (AIDA), a framework that treats intermediate-domain learning as a dynamically regulated process. It adaptively controls feature mixing and regularization using feedback signals, and synthesizes diverse intermediate representations with a pseudo-mirror regularization strategy. This approach is demonstrated to be effective across domain generalization and source-free settings, offering significant potential for real-world surveillance and security applications where models need to perform well in unseen environments without access to original training data.