# Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification

arXiv: https://arxiv.org/abs/2105.11722 · HTML: https://mlsanity.com/paper/2105.11722/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 64 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification](https://mlsanity.com/paper/2001.01526/) (2020)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification](https://mlsanity.com/paper/2003.08177/) (2020)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification](https://mlsanity.com/paper/2004.08787/) (2020)
- [FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification](https://mlsanity.com/paper/1810.02936/) (2018)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [Re-Identification with Consistent Attentive Siamese Networks](https://mlsanity.com/paper/1811.07487/) (2018)
- [COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching](https://mlsanity.com/paper/2006.08765/) (2020)
- [Adaptive Graph Representation Learning for Video Person Re-identification](https://mlsanity.com/paper/1909.02240/) (2019)
- [SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images](https://mlsanity.com/paper/1912.09121/) (2019)
- [Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification](https://mlsanity.com/paper/1908.06052/) (2019)
- [A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification](https://mlsanity.com/paper/2007.01504/) (2020)
- [Learning Resolution-Invariant Deep Representations for Person Re-Identification](https://mlsanity.com/paper/1907.10843/) (2019)
- [CDPM: Convolutional Deformable Part Models for Semantically Aligned Person Re-identification](https://mlsanity.com/paper/1906.04976/) (2019)
- [Resolution-invariant Person Re-Identification](https://mlsanity.com/paper/1906.09748/) (2019)
- [Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond](https://mlsanity.com/paper/2002.09274/) (2020)
- [Receptive Multi-granularity Representation for Person Re-Identification](https://mlsanity.com/paper/2008.13450/) (2020)

## Built on by (most cited first, 14 of 14)

- [CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification](https://mlsanity.com/paper/2110.08994/) (2021)
- [A Versatile Framework for Multi-scene Person Re-identification](https://mlsanity.com/paper/2403.11121/) (2024)
- [Video Person Re-identification using Attribute-enhanced Features](https://mlsanity.com/paper/2108.06946/) (2021)
- [Label Distribution Learning for Generalizable Multi-source Person Re-identification](https://mlsanity.com/paper/2204.05903/) (2022)
- [Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification](https://mlsanity.com/paper/2207.13037/) (2022)
- [DynaMix: Generalizable Person Re-identification via Dynamic Relabeling and Mixed Data Sampling](https://mlsanity.com/paper/2511.19067/) (2025)
- [PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2310.07743/) (2023)
- [Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification](https://mlsanity.com/paper/2510.00936/) (2025)
- [Generalizable Metric Network for Cross-domain Person Re-identification](https://mlsanity.com/paper/2306.11991/) (2023)
- [A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets](https://mlsanity.com/paper/2209.01848/) (2022)
- [Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2112.06147/) (2021)
- [DaliID: Distortion-Adaptive Learned Invariance for Identification Models](https://mlsanity.com/paper/2302.05753/) (2023)
- [S3-CLIP: Video Super Resolution for Person-ReID](https://mlsanity.com/paper/2601.08807/) (2026)
- [Reliability-Aware 3D Geometric Injection for Universal Person Re-identification](https://mlsanity.com/paper/2607.18863/) (2026)

## Abstract (arXiv)

Person re-identification (re-ID) tackles the problem of matching person images with the same identity from different cameras. In practical applications, due to the differences in camera performance and distance between cameras and persons of interest, captured person images usually have various resolutions. We name this problem as Cross-Resolution Person Re-identification which brings a great challenge for matching correctly. In this paper, we propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the above problem. Specifically, in order to restore the resolution of low-resolution images and make reasonable use of different channel information of feature maps, we introduce and innovate VDSR module with channel attention (CA) mechanism, named as VDSR-CA. Then we reform the HRNet by designing a novel representation head to extract discriminating features, named as HRNet-ReID. In addition, a pseudo-siamese framework is constructed to reduce the difference of feature distributions between low-resolution images and high-resolution images. The experimental results on five cross-resolution person datasets verify the effectiveness of our proposed approach. Compared with the state-of-the-art methods, our proposed PS-HRNet improves 3.4%, 6.2%, 2.5%,1.1% and 4.2% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively. Our code is available at urlhttps://github.com/zhguoqing.