TCRNet: An Efficient Vehicle Re-Identification Network Using TinyCLIP-Assisted Feature Representation

Vehicle re-identification (ReID) is an important component of intelligent transportation systems, yet existing methods often struggle with challenges such as inter-class similarity, viewpoint variations, and environmental noise. While CLIP-based approaches have shown promise in bridging visual and semantic domains, they often rely heavily on the pre-trained CLIP model, limiting flexibility and integration with standard baselines. To address this, we propose TCRNet, an efficient vehicle ReID network that integrates semantic features from TinyCLIP into the visual features extracted by a baseline ResNet50-IBN model. By fusing TinyCLIP-assisted semantic features with visual representations in an end-to-end training pipeline, TCRNet demonstrates strong capability in robust and discriminative feature learning. Extensive ablation experiments on the VeRi-776 dataset validate the discriminative ability of the proposed model, achieving 89.7% mAP and 97.5% Rank-1.