# Multi-Scale Body-Part Mask Guided Attention for Person Re-identification

arXiv: https://arxiv.org/abs/1904.11041 · HTML: https://mlsanity.com/paper/1904.11041/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)
- [Deep Learning-based Occluded Person Re-identification: A Survey](https://mlsanity.com/paper/2207.14452/) (2022)
- [OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification](https://mlsanity.com/paper/2109.11159/) (2021)
- [Holistic Guidance for Occluded Person Re-Identification](https://mlsanity.com/paper/2104.06524/) (2021)
- [Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification](https://mlsanity.com/paper/2211.14742/) (2022)
- [MGA-Net: A Novel Mask-Guided Attention Neural Network for Precision Neonatal Brain Imaging](https://mlsanity.com/paper/2406.17709/) (2024)
- [MammoTracker: Mask-Guided Lesion Tracking in Temporal Mammograms](https://mlsanity.com/paper/2507.00328/) (2025)

## Abstract (arXiv)

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.