# Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification

arXiv: https://arxiv.org/abs/1908.06052 · HTML: https://mlsanity.com/paper/1908.06052/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [Large-Scale Pre-training for Person Re-identification with Noisy Labels](https://mlsanity.com/paper/2203.16533/) (2022)
- [Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2208.02450/) (2022)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification](https://mlsanity.com/paper/2105.11722/) (2021)
- [A Versatile Framework for Multi-scene Person Re-identification](https://mlsanity.com/paper/2403.11121/) (2024)
- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)
- [Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars](https://mlsanity.com/paper/2207.07896/) (2022)
- [Towards Generalizable Person Re-identification with a Bi-stream Generative Model](https://mlsanity.com/paper/2206.09362/) (2022)
- [Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification](https://mlsanity.com/paper/2105.12684/) (2021)
- [Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification](https://mlsanity.com/paper/2207.13037/) (2022)
- [Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID](https://mlsanity.com/paper/2505.12580/) (2025)
- [Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification](https://mlsanity.com/paper/2510.00936/) (2025)
- [Resolution based Feature Distillation for Cross Resolution Person Re-Identification](https://mlsanity.com/paper/2109.07871/) (2021)
- [Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation](https://mlsanity.com/paper/2409.12385/) (2024)
- [Resolution-invariant Person ReID Based on Feature Transformation and Self-weighted Attention](https://mlsanity.com/paper/2101.04544/) (2021)
- [Unsupervised clothing change adaptive person ReID](https://mlsanity.com/paper/2109.03702/) (2021)
- [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) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade person re-ID performance in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover the missing details in low-resolution input images. The resulting features can be jointly applied for improving person re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Our experiments on five benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art methods, especially when the input resolutions are unseen during training.