Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification

Jianyuan Guo, Yuhui Yuan, Lang Huang, Chao Zhang, Jin-Ge Yao, Kai Han; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 3642-3651

Abstract


Person re-identification is a challenging task due to various complex factors. Recent studies have attempted to integrate human parsing results or externally defined attributes to help capture human parts or important object regions. On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes. In this paper, we address the missed contextual cues by exploiting both the accurate human parts and the coarse non-human parts. In our implementation, we apply a human parsing model to extract the binary human part masks and a self-attention mechanism to capture the soft latent (non-human) part masks. We verify the effectiveness of our approach with new state-of-the-art performance on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03. Our implementation is available at https://github.com/ggjy/P2Net.pytorch.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Guo_2019_ICCV,
author = {Guo, Jianyuan and Yuan, Yuhui and Huang, Lang and Zhang, Chao and Yao, Jin-Ge and Han, Kai},
title = {Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}