Papers › Pyramidal Transformer with Conv-Patchify for Person Re-identification
Pyramidal Transformer with Conv-Patchify for Person Re-identification
He Li, Mang Ye, Cong Wang, Bo Do
The robust and discriminative feature extraction is the key component in person re-identification (Re-ID). The major weakness ofconventional convolution neural network (CNN) based methods is that they cannot extract long-range information from diverse parts, which can be alleviated by recently developed Transformers. Existing vision Transformers show their power on various vision tasks. However, they (i) cannot address translation problems and different viewpoints; (ii) cannot capture detailed features to discriminate people with a similar appearance. In this paper, we propose a powerful Re-ID baseline built on top of the pyramidal transformer with conv-patchify operation, termed PTCR, which inherits the advantages of both CNN and Transformer. The pyramidal structure captures multi-scale fine-grained features, while the convpatchify enhances the robustness against translation. Moreover, we additionally design two novel modules to improve the robust feature learning. A Token Perception module augments the patch embeddings to enhance the robustness against perturbation and viewpoint changes, while the Auxiliary Embedding module integrates the auxiliary information (cam ID, pedestrian attributes, etc.)to reduce feature bias caused by non-visual factors. Our method is validated through extensive experiments to show its superior performance with abundant ablation studies. Notably, without re-ranking, we achieve 98.0% Rank-1 on Market-1501 and 88.6% Rank-1 on MSMT17, significantly outperforming the counterparts. The code is available at: https://github.com/lihe404/PTCR
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections