关键词: 车辆重识别, 视觉Transformer, 多尺度特征融合, 交叉注意力机制, 多分支联合监督

Abstract: To address the issues of insufficient utilization of intermediate spatial information and inadequate modeling of local discriminative features in Vehicle Re-Identification (Vehicle Re-ID) under multi-view conditions, a Multi-Scale feature-guided Adaptive Local queries (MSAL) method for vehicle re-identificationwas proposed. In this method, a Vision Transformer (ViT) was used as the backbone network to extract image patch features from different depths. A hierarchical spatial representation was constructed through a Feature Pyramid Network-based Multi-Scale Fusion (FPN-MSF) module to enhance the utilization of intermediate spatial information. Furthermore, a Serial Bidirectional Cross-Attention (SBCA) mechanism was designed, and local queries were introduced to perform a three-stage serial interaction with multi-scale features, so that more stable local discriminative information was gradually extracted. Simultaneously, a multi-branch joint supervision strategy was employed to collaboratively optimize global and local features, so that the discriminative power and robustness of the feature representation were improved. Experimental results show that on the VeRi-776 dataset, the proposed method achieves mean Average Precision (mAP) of 84.2% and Rank-1 matching accuracy (Rank-1) of 97.7%, which are 2.8 and 0.7 percentage points higher than those of Patch-Mixup-ViT, respectively. On the Test800, Test1600 and Test2400 subsets of the VehicleID dataset, the Rank-1 of the proposed method reaches 83.8%, 80.2% and 77.2%, respectively, which are 0.8, 0.8 and 1.1 percentage points higher than those of Id-BGFNet, respectively. The results validate the effectiveness and generalization ability of the proposed method in multi-view vehicle re-identification tasks.

Key words: vehicle re-identification, Vision Transformer (ViT), multi-scale feature fusion, cross-attention mechanism, multi-branch joint supervision