# Illumination-Adaptive Person Re-identification

arXiv: https://arxiv.org/abs/1905.04525 · HTML: https://mlsanity.com/paper/1905.04525/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2019.

## Built on by (most cited first, 21 of 21)

- [Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2107.02380/) (2021)
- [CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification](https://mlsanity.com/paper/2110.08994/) (2021)
- [Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval](https://mlsanity.com/paper/2205.10878/) (2022)
- [ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models](https://mlsanity.com/paper/2502.19958/) (2025)
- [A Versatile Framework for Multi-scene Person Re-identification](https://mlsanity.com/paper/2403.11121/) (2024)
- [Shape-centered Representation Learning for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2310.17952/) (2023)
- [Towards Generalizable Person Re-identification with a Bi-stream Generative Model](https://mlsanity.com/paper/2206.09362/) (2022)
- [Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark](https://mlsanity.com/paper/2308.16486/) (2023)
- [Exploring Part-Informed Visual-Language Learning for Person Re-Identification](https://mlsanity.com/paper/2308.02738/) (2023)
- [A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer](https://mlsanity.com/paper/2109.11719/) (2021)
- [Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification](https://mlsanity.com/paper/2207.14243/) (2022)
- [Lifelong Person Re-Identification with Backward-Compatibility](https://mlsanity.com/paper/2403.10022/) (2024)
- [Resolution-invariant Person ReID Based on Feature Transformation and Self-weighted Attention](https://mlsanity.com/paper/2101.04544/) (2021)
- [Real-time Human-Centric Segmentation for Complex Video Scenes](https://mlsanity.com/paper/2108.07199/) (2021)
- [Cross-Camera Trajectories Help Person Retrieval in a Camera Network](https://mlsanity.com/paper/2204.12900/) (2022)
- [Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval](https://mlsanity.com/paper/2211.14515/) (2022)
- [Mix-Modality Person Re-Identification: A New and Practical Paradigm](https://mlsanity.com/paper/2412.04719/) (2024)
- [Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network](https://mlsanity.com/paper/2504.20568/) (2025)
- [Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2112.06147/) (2021)
- [Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2403.08270/) (2024)
- [GeoMetric: Injecting Metric Geographic Structure into Worldwide Image Geo-Localization](https://mlsanity.com/paper/2606.08918/) (2026)

## Abstract (arXiv)

Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common, and person images are often captured under different illumination conditions at different times across a day. In this situation, the performances of existing ReID models often degrade dramatically. This paper addresses the ReID problem with illumination variations and names it as em Illumination-Adaptive Person Re-identification (IA-ReID). We propose an Illumination-Identity Disentanglement (IID) network to dispel different scales of illuminations away while preserving individuals' identity information. To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations. Experimental results on the simulated datasets and real-world images demonstrate the effectiveness of the proposed framework.