Global-Local Unified Cross-View Enhancing Framework for Person Re-Identification

In Pedestrian Re-identification (ReID) tasks, extracting robust and discriminative features is a key challenge. Recent studies have mainly concentrated on the extraction of features from single images. However, limited research has been done on considering the relationships between different images, which is crucial in the ReID task. To effectively integrate features from multiple perspectives, we propose an explicitly feature enhancing strategy, Global-local unified cross-view enhancing(GLE) framework for person re-identification, based on Transformer among images sharing the same identity. Specifically, our method includes two aspects: (i) Multi-scale global feature enhancement(MSGE): It displays enhancing global features by interactively updating the features of different images sharing the same identity. (ii) Key local feature enhancement(KLFE): It aims to enhance local feature interaction in key areas by selecting and updating top-k patches based on attention scores derived from thermal maps in each training stage. Experimental results demonstrate that our method can extract more robust and discriminative features, achieving state-of-the-art performance on four pedestrian re-identification datasets.