Cross-Visual Attention Fusion Network with Dual-Constrained Marginal-Ranking for Visible-Infrared Person Re-Identification

Visible-Infrared Person re-identification(VI-REID) is extremely important for night-time surveillance applications. It is a challenging problem due to large cross-modality discrepancies and intra-modality variations caused by different illuminations, human poses, viewpoints, etc. In this paper, we propose a cross visual attention fusion dual-path neural network with dual-constrained marginal ranking(DCAF) to solve the problem. First, we utilize cross-visual attention to learn discriminative feature of high-level semantic information in their respective modals. Second, in order to establish the relationship between modals, we fuse attentional weight of two modals and add it into backpropagation to obtain those regions that are distinctive for classification. Third, a dual-constrained marginal-ranking loss is introduced to narrow the gap between different networks and to learn strongly the similarities between two modals. Extensive experiments demonstrate that the proposed approach effectively improves the performance of VI-REID task and remarkably outperforms the state-of-the-art methods.