A Dual Self-Attention mechanism for vehicle re-Identification

•A novel multi-attention network simulating the visual attention of humans is designed to realize highly efficient alignment and feature embedding globally and locally for vehicle re-ID, improving the performance of re-ID both contextually and spatially.•We propose a dual self-attention mechanism where the static self-attention and the dynamicself-attention (named cross-region attention) are designed to capture long-range dependenciesglobally and the position-related-range dependencies locally respectively, so as to enhance thespatial/positional awareness of our model.•We conduct extensive experiments to validate the effectiveness of our model and show that the proposed method achieves the best experimental results on most metrics compared withmany other state-of-the-art methods, e.g., Rank-1 accuracy of 80.6%, 78.1%, 75.0% on three testing subsets of VehicleID and 92.9%, 91.8%, 90.4% on those of Vehicle-1M. Our model also achieves 76.3% mAP and 94.8% on Veri776.