  Vision Transformers (ViTs) have excelled in vehicle re-identification (ReID)
tasks. However, non-square aspect ratios of image or video input might
significantly affect the re-identification performance. To address this issue,
we propose a novel ViT-based ReID framework in this paper, which fuses models
trained on a variety of aspect ratios. Our main contributions are threefold:
(i) We analyze aspect ratio performance on VeRi-776 and VehicleID datasets,
guiding input settings based on aspect ratios of original images. (ii) We
introduce patch-wise mixup intra-image during ViT patchification (guided by
spatial attention scores) and implement uneven stride for better object aspect
ratio matching. (iii) We propose a dynamic feature fusing ReID network,
enhancing model robustness. Our ReID method achieves a significantly improved
mean Average Precision (mAP) of 91.0\% compared to the the closest
state-of-the-art (CAL) result of 80.9\% on VehicleID dataset.
