부산대학교 도서관

Vehicle re-identification’s methods usually exploit the spatial uniform partition strategy via dividing deep feature maps into several parts. Then each of them is further independently processed by the multi-network branch to obtain refined part features. However, the cooperation among those part features is underestimated. This paper proposes a contrastive attention module (CAM) to assess one part feature’s importance based on all parts. Practical cooperation among part features is derived by re-weighting the part feature. Furthermore, a flexible CAM network (CAMNet) compatible with contrastive attention module is proposed to enhance part features for vehicle re-identification. Extensive experiments show that the proposed CAMNet method outperforms many state-of-the-art vehicle re-identification approaches.