Domain Adaptive Person Re Identification

Domain adaptive person re-identification (ReID) focuses on adapting person recognition models trained on one dataset (source domain) to perform well on a different, unlabeled dataset (target domain), overcoming visual discrepancies between the domains. Current research emphasizes unsupervised approaches, employing techniques like pseudo-labeling, clustering, and teacher-student frameworks to leverage unlabeled target data, often incorporating novel loss functions or memory mechanisms to improve robustness and accuracy. These advancements are crucial for building more practical and generalizable person ReID systems, reducing the reliance on extensive manual annotation and enabling deployment across diverse surveillance scenarios.