DSA-SCGC: A Dual Self-Attention Mechanism based on Space-Channel Grouped Compression for Vehicle Re-Identification

Vehicle re-identification (re-ID) has attracted significant attention within the computer vision community due to its wide-ranging applications in intelligent transportation systems and law enforcement. Nevertheless, this field faces considerable challenges owing to the high inter-class similarity and the large intra-class difference among vehicles. To address these challenges, this paper proposes a novel network incorporating a dual self-attention mechanism based on a space-channel grouped compression operation (DSA-SCGC). This innovative approach combines channel and spatial self-attention mechanisms to selectively enhance pivotal channel features and spatial local details while minimizing attention toward backgrounds and occlusions commonly encountered in real-world scenarios. Moreover, to address the issue of spatial information loss in channel attention, we propose a space-channel grouped compression (SCGC) operation that effectively compresses spatial information into channels, thereby significantly preserving spatial information. Comprehensive experiments conducted on the VeRi-776 and VehicleID datasets validate the superiority of our proposed DSA-SCGC model over the existing state-of-the-art vehicle re-identification methods.