# SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification

arXiv: https://arxiv.org/abs/1810.06996 · HTML: https://mlsanity.com/paper/1810.06996/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021)
- [Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification](https://mlsanity.com/paper/2108.00139/) (2021)
- [Domain-Class Correlation Decomposition for Generalizable Person Re-Identification](https://mlsanity.com/paper/2106.15206/) (2021)
- [Exploiting Robust Unsupervised Video Person Re-identification](https://mlsanity.com/paper/2111.05170/) (2021)
- [Global-Local Dynamic Feature Alignment Network for Person Re-Identification](https://mlsanity.com/paper/2109.05759/) (2021)
- [Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2410.03977/) (2024)
- [Dynamic Template Initialization for Part-Aware Person Re-ID](https://mlsanity.com/paper/2208.11440/) (2022)
- [PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method](https://mlsanity.com/paper/2303.06330/) (2023)

## Abstract (arXiv)

Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.