Beyond Global-Cluster Supervision: Adaptive Feature Fusion and Memory Augmentation for Unsupervised Person Re-Identification

Abstract

As a critical task in intelligent surveillance and smart city systems, person re-identification (ReID) addresses the challenge of matching individuals across non-overlapping camera views. Although fully unsupervised learning (USL) methods based on pseudo-label training have achieved remarkable progress, they still face three persistent challenges: accumulated noise in pseudo-labels, insufficient modeling of local features, and poor adaptability of cluster-level memory banks. To this end, we present a framework named Adaptive Feature Fusion and Memory Augmentation (AF2MA), which consists of two core modules: adaptive feature fusion and memory augmentation. Specifically, the adaptive feature fusion module enhances robustness to pose and occlusion by decoupling the feature map into coarse spatial regions and recombining regional feature representations to generate predicted features, thereby deriving more reliable pseudo-labels. The memory augmentation module suppresses noise and facilitates complementary learning through independently optimized cluster-level and instance-level memory banks, effectively alleviating the stability-plasticity dilemma. Furthermore, we design a teacher-student learning strategy to provide stable, task-specific supervision and mitigate the accumulation of pseudo-label errors during iterative training. Extensive experiments validate the superiority of our proposed method.