Multi-Scale Relation Network for Person Re-identification

Person re-identification (reID) has received extensive study and achieved great progress in recent years. Extensive research has proved that combining global and local features is an effective solution to improve the performance of person reidentification tasks. While many existing reID approaches are still suffering from occlusions, body part missing, different lighting, and background clutter, where the learned features may achieve a sub-optimal solution. In this paper, we propose an efficient network structure, Multi-Scale Relation Network (MSRN), which can not only extract robust regional features and global features but also integrate the asymptotic cues and relations between them. In addition, we introduce a dynamic loss weight as supplementary components to improve learning efficiency and the representation capacity of our model. Extensive experiments are conducted on three widely used datasets, including Market-I501, DukeMTMC-ReID and CUHK03-NP. The experimental results indicate that our proposed method achieves the state-of-the-art results on three datasets.