Dynamic Feature Weakening for Cross-Modality Person Re-Identification

Cross-modality person re-identification (Re-ID) can be used to perform all-weather pedestrian monitoring tasks by matching the visual features captured in both visible and infrared images. However, the existing mainstream methods mainly rely on a few high response modality-invariant features, leading to problems such as easy loss of person cues and poor algorithm robustness in the recognition process. In this paper, we propose a cross-modality recognition method with dynamic feature weakening (DFW) and flexible segmentation learning as the core mechanisms, specifically: (1) DFW is introduced into the modality-shared network to weaken the model’s over-reliance on high response regions of the image, thereby globally collecting and enriching diverse modality-invariant features. (2) A segmented learning network (SLN) with overlap penalty constraint is used for fine-grained mining of each modality-invariant feature, while avoiding redundant focus on a region and maintaining complete learning of diversity information. The experimental results show that the method in this paper greatly enriches the feature representation, and significantly improves the recognition accuracy of the model. On the SYSU-MM01 dataset, compared with the traditional modality feature learning method, the Rank1 and mAP of this method are increased by 2.56% and 4.53%, respectively.