Multi-Scale Body-Part Mask Guided Attention for Person Re-identification
arXiv:1904.11041
Abstract
Person re-identification becomes a more and more important task due to its wide applications. In practice, person re-identification still remains challenging due to the variation of person pose, different lighting, occlusion, misalignment, background clutter, etc. In this paper, we propose a multi-scale body-part mask guided attention network (MMGA), which jointly learns whole-body and part body attention to help extract global and local features simultaneously. In MMGA, body-part masks are used to guide the training of corresponding attention. Experiments show that our proposed method can reduce the negative influence of variation of person pose, misalignment and background clutter. Our method achieves rank-1/mAP of 95.0%/87.2% on the Market1501 dataset, 89.5%/78.1% on the DukeMTMC-reID dataset, outperforming current state-of-the-art methods.
References in corpus (7)
- In Defense of the Triplet Loss for Person Re-Identification
- Improving Person Re-identification by Attribute and Identity Learning
- Random Erasing Data Augmentation
- Deep Representation Learning with Part Loss for Person Re-Identification
- AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
- Horizontal Pyramid Matching for Person Re-identification
- Person Search via A Mask-Guided Two-Stream CNN Model