# Spatial-Aware GAN for Unsupervised Person Re-identification

arXiv: https://arxiv.org/abs/1911.11312 · HTML: https://mlsanity.com/paper/1911.11312/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [Diverse Image Inpainting with Bidirectional and Autoregressive Transformers](https://mlsanity.com/paper/2104.12335/) (2021)
- [Unbalanced Feature Transport for Exemplar-based Image Translation](https://mlsanity.com/paper/2106.10482/) (2021)
- [Blind Image Super-Resolution via Contrastive Representation Learning](https://mlsanity.com/paper/2107.00708/) (2021)
- [GMLight: Lighting Estimation via Geometric Distribution Approximation](https://mlsanity.com/paper/2102.10244/) (2021)
- [Bi-level Feature Alignment for Versatile Image Translation and Manipulation](https://mlsanity.com/paper/2107.03021/) (2021)
- [Sparse Needlets for Lighting Estimation with Spherical Transport Loss](https://mlsanity.com/paper/2106.13090/) (2021)
- [Uncertainty-aware Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2307.15409/) (2023)
- [Auto-regressive Image Synthesis with Integrated Quantization](https://mlsanity.com/paper/2207.10776/) (2022)
- [Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting](https://mlsanity.com/paper/2104.03520/) (2021)
- [FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition](https://mlsanity.com/paper/2107.03166/) (2021)
- [Image-to-image Transformation with Auxiliary Condition](https://mlsanity.com/paper/2106.13696/) (2021)
- [A Survey on Adversarial Image Synthesis](https://mlsanity.com/paper/2106.16056/) (2021)
- [Scene Text recognition with Full Normalization](https://mlsanity.com/paper/2109.01034/) (2021)

## Abstract (arXiv)

The recent person re-identification research has achieved great success by learning from a large number of labeled person images. On the other hand, the learned models often experience significant performance drops when applied to images collected in a different environment. Unsupervised domain adaptation (UDA) has been investigated to mitigate this constraint, but most existing systems adapt images at pixel level only and ignore obvious discrepancies at spatial level. This paper presents an innovative UDA-based person re-identification network that is capable of adapting images at both spatial and pixel levels simultaneously. A novel disentangled cycle-consistency loss is designed which guides the learning of spatial-level and pixel-level adaptation in a collaborative manner. In addition, a novel multi-modal mechanism is incorporated which is capable of generating images of different geometry views and augmenting training images effectively. Extensive experiments over a number of public datasets show that the proposed UDA network achieves superior person re-identification performance as compared with the state-of-the-art.