Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
arXiv:2106.04095
Abstract
Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoderdecoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder. The proposed PAT model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit the transformer encoder-decoder architecture for occluded person Re-ID in a unified deep model. Second, to learn part prototypes well with only identity labels, we design two effective mechanisms including part diversity and part discriminability. Consequently, we can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner. Extensive experimental results on six challenging benchmarks for three tasks (occluded, partial and holistic Re-ID) demonstrate that our proposed PAT performs favorably against stat-of-the-art methods.
arXiv commentAccepted by CVPR 2021
References in corpus (6)
- In Defense of the Triplet Loss for Person Re-Identification
- Person Re-identification: Past, Present and Future
- FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
- Recognizing Partial Biometric Patterns
- A Novel Teacher-Student Learning Framework For Occluded Person Re-Identification
- Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification