Lightweight network for visible-infrared person re-identification via self-distillation and multi-granularity information mining

The task of visible and infrared person re-identification (VI-ReID) aims to retrieve person images across visible and infrared images. However, the significant modality discrepancy and intra-modality variations render this task extremely challenging. Existing VI-ReID methods ignore the design for lightweight network. To address the above problems, we design a lightweight two-stream network based on omni-scale network (OSNet) for this task, we further explore how many parameters are shared is more efficient for two-stream network. On this basis, we propose a novel self-distillation module (SDM) to improve the feature extraction capability of this two-stream network. The SDM introduces the deepest classifier as a teacher model and constructs three shallow classifiers as student models. Under the guidance of the teacher model, these student models absorb rich deep knowledge from the deepest classifier to achieve optimization of low-level features, thus promoting the improvement of high-level feature representation. Subsequently, in order to extract highly discriminative part-informed features, we introduce a multi-granularity information mining(MGIM) block that not only learns local features but also considers the internal relationships between local features. This helps to fully mine local detail information within the images. The extensive experiments on the SYSU-MM01,RegDB,and LLCM datasets show that our proposed method achieves superior performance.