CFViT: Coarse and Fine-grained Vision Transformer for Vehicle Re-Identification

Identifying vehicles across multiple non-overlapping camera views is a crucial task in computer vision, with important applications in public safety, urban surveillance, and intelligent transportation systems. Traditional methods are prone to errors under variations in scale, illumination, complex background, and object occlusion scenarios. To address these limitations, we propose a new framework, Coarse and Fine-grained Vision Transformer (CFViT). This framework efficiently captures coarse-grained(global) and fine-grained (local) features. We then develop a Coarse Fine Grained Module (CFGM) that performs shuffle and shift operations on vehicle patch images, resulting in robust feature representation ensuring robustness against variations in vehicle appearance. We further encode side information of vehicles into the Faster Vision Transformer (FasterViT) model to prevent failures in distinguishing the same object from different viewpoints. To validate the effectiveness of the proposed method, we achieve state-of-the-art performance on two publicly available datasets, including VehicleID, and Veri-776.