DualDis: A Dual Disentanglement Network for Vehicle Re-identification

Vehicle re-identification (ReID) plays a key role in intelligent transportation systems. However, this task is complicated by intra-class variations due to viewpoint changes, occlusions, and inter-class differences among visually similar vehicles. The issues of feature coupling and limited fine-grained discrimination affect accurate vehicle matching. We propose DualDis, a novel vehicle re-identification framework that decouples identity-related features. DualDis consists of two key modules, Adaptive Component Disentangling (ACD) and Progressive Dimensional Attention (PDA). ACD uses multi-head attention to separate vehicle parts, while PDA deploys a region-aware sparse channel and symmetry-aware contextual attention to distinguish symmetrical and asymmetrical features. This dual-path structure enables the given model to concentrate on the most discriminative features while minimizing redundant information. Extensive experiments on the VeRi776 and VehicleID datasets reveal that DualDis outperforms state-of-the-art methods in multi-view retrieval, which obtains superior accuracy and demonstrates its generalization capabilities across different datasets. The source code is publicly available at https://github.com/711L/DualDis.