MULTI-LEVEL RELATION AWARE NETWORK FOR PERSON RE-IDENTIFICATION

Person attribute or pose information has improved person re-identification performance, however, inaccurate pose or attribute module will damage the final identification performance. Based on this, we propose a multi-scale relation aware network (MSRA) for person re-identification. Specifically, we design an attribute relation mining module to construct an attribute map through constraint loss to learn the correlation between different attributes. Besides, we construct a multi-level Pose Pyramid based on the physical structure of the human body, so as to model the internal relationship between pose points. Finally, we designed a cross-scale graph convolution to infer the cooperative structural relation between different layers of components and fused it with the attribute relation module to reinforce the feature. Many experiments on three large-scale datasets verify the effectiveness and state-of-the-art performance of the proposed method.