# Attention Driven Person Re-identification

arXiv: https://arxiv.org/abs/1810.05866 · HTML: https://mlsanity.com/paper/1810.05866/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [Part-based Pseudo Label Refinement for Unsupervised Person Re-identification](https://mlsanity.com/paper/2203.14675/) (2022)
- [Feature Erasing and Diffusion Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2112.08740/) (2021)
- [Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search](https://mlsanity.com/paper/2208.14365/) (2022)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Rotation Invariant Transformer for Recognizing Object in UAVs](https://mlsanity.com/paper/2311.02559/) (2023)
- [A Brief Survey on Person Recognition at a Distance](https://mlsanity.com/paper/2212.08969/) (2022)
- [BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention](https://mlsanity.com/paper/2106.02566/) (2021)
- [MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2311.14395/) (2023)
- [BBA-net: A bi-branch attention network for crowd counting](https://mlsanity.com/paper/2201.08983/) (2022)
- [A Little Bit Attention Is All You Need for Person Re-Identification](https://mlsanity.com/paper/2302.14574/) (2023)
- [Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology](https://mlsanity.com/paper/2506.04143/) (2025)

## Abstract (arXiv)

Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc. It has been studied extensively in recent years, but the multifarious local and global features are still not fully exploited by either ignoring the interplay between whole-body images and body-part images or missing in-depth examination of specific body-part images. In this paper, we propose a novel attention-driven multi-branch network that learns robust and discriminative human representation from global whole-body images and local body-part images simultaneously. Within each branch, an intra-attention network is designed to search for informative and discriminative regions within the whole-body or body-part images, where attention is elegantly decomposed into spatial-wise attention and channel-wise attention for effective and efficient learning. In addition, a novel inter-attention module is designed which fuses the output of intra-attention networks adaptively for optimal person ReID. The proposed technique has been evaluated over three widely used datasets CUHK03, Market-1501 and DukeMTMC-ReID, and experiments demonstrate its superior robustness and effectiveness as compared with the state of the arts.