# Learning Resolution-Invariant Deep Representations for Person Re-Identification

arXiv: https://arxiv.org/abs/1907.10843 · HTML: https://mlsanity.com/paper/1907.10843/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

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

- [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)
- [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)
- [LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models](https://mlsanity.com/paper/2502.03950/) (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)
- [Resolution-invariant Person ReID Based on Feature Transformation and Self-weighted Attention](https://mlsanity.com/paper/2101.04544/) (2021)
- [Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing](https://mlsanity.com/paper/2402.08025/) (2024)

## Abstract (arXiv)

Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios might suffer from resolution loss, how to solve the resolution mismatch problem during person re-ID becomes a practical problem. Instead of applying separate image super-resolution models, we propose a novel network architecture of Resolution Adaptation and re-Identification Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy of adversarial learning, we aim at extracting resolution-invariant representations for re-ID, while the proposed model is learned in an end-to-end training fashion. Our experiments confirm that the use of our model can recognize low-resolution query images, even if the resolution is not seen during training. Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.