AAGCN: Adjacency-aware Graph Convolutional Network for person re-identification

Person re-identification (ReID) is an important topic of computer vision. Existing works in this field focus primarily on learning a feature extractor that maps the pedestrian images into a feature space, in which feature vectors corresponding to the same identity are close to each other. In this paper, we propose the adjacency-aware Graph Convolutional Network (AAGCN) to smooth the intra-class features and thus reduce the intra-class variance. Specifically, our AAGCN takes the features learned by a backbone as the input nodes; it first establishes the connections or adjacency relations for the intra-class features, then the adjacent nodes (i.e., the intra-class features) would be smoothed thanks to the property of low-pass filtering of Graph Convolutional Network (GCN). In this paper, we propose two methods, i.e., the Mahalanobis Neighborhood Adjacency (MNA) and Non-Linear Mapping (NLM), to learn the adjacency relations for the intra-class features. The MNA defines the adjacency weight between two nodes as the negative exponent of the Mahalanobis distance between their corresponding features, therefore it aims to learn a small Mahalanobis distance between the intra-class features and a large Mahalanobis distance between the inter-class ones. The NLM enables the non-linear mapping from the features of the nodes to their corresponding adjacency weights. The experimental results on both visible ReID and visual-infrared ReID verify the effectiveness of our method, for instance, our model achieves 95.7% rank-1 and 93.1% mAP on Market1501, as well as 58.6% rank-1 and 60.0% mAP on SYSU. (c) 2021 Published by Elsevier B.V.