# Parameter-Free Spatial Attention Network for Person Re-Identification

arXiv: https://arxiv.org/abs/1811.12150 · HTML: https://mlsanity.com/paper/1811.12150/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [Person Re-Identification with a Locally Aware Transformer](https://mlsanity.com/paper/2106.03720/) (2021)
- [Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning](https://mlsanity.com/paper/2212.14284/) (2022)
- [Parameter-Free Channel Attention for Image Classification and Super-Resolution](https://mlsanity.com/paper/2303.11055/) (2023)
- [Pose Invariant Person Re-Identification using Robust Pose-transformation GAN](https://mlsanity.com/paper/2105.00930/) (2021)
- [Interflow: Aggregating Multi-layer Feature Mappings with Attention Mechanism](https://mlsanity.com/paper/2106.14073/) (2021)
- [FPB: Feature Pyramid Branch for Person Re-Identification](https://mlsanity.com/paper/2108.01901/) (2021)
- [CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion](https://mlsanity.com/paper/2212.06335/) (2022)
- [Efficient Human Pose Estimation by Maximizing Fusion and High-Level Spatial Attention](https://mlsanity.com/paper/2107.13693/) (2021)
- [Discriminative Feature Learning through Feature Distance Loss](https://mlsanity.com/paper/2205.11606/) (2022)
- [SATBA: An Invisible Backdoor Attack Based On Spatial Attention](https://mlsanity.com/paper/2302.13056/) (2023)
- [Exploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification](https://mlsanity.com/paper/2510.19463/) (2025)
- [Optimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport](https://mlsanity.com/paper/2605.18349/) (2026)

## Abstract (arXiv)

Global average pooling (GAP) allows to localize discriminative information for recognition [40]. While GAP helps the convolution neural network to attend to the most discriminative features of an object, it may suffer if that information is missing e.g. due to camera viewpoint changes. To circumvent this issue, we argue that it is advantageous to attend to the global configuration of the object by modeling spatial relations among high-level features. We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model. Our spatial attention layer consistently improves the performance over the model without it. Results on four benchmarks demonstrate a superiority of our model over the state-of-the-art achieving rank-1 accuracy of 94.7% on Market-1501, 89.0% on DukeMTMC-ReID, 74.9% on CUHK03-labeled and 69.7% on CUHK03-detected.