# Visible-Infrared Person Re-Identification Using Privileged Intermediate Information

arXiv: https://arxiv.org/abs/2209.09348 · HTML: https://mlsanity.com/paper/2209.09348/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2007.09314/) (2020)
- [RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment](https://mlsanity.com/paper/1910.05839/) (2019)
- [Cross-modality Person re-identification with Shared-Specific Feature Transfer](https://mlsanity.com/paper/2002.12489/) (2020)
- [Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2002.04114/) (2020)
- [Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/1912.01230/) (2019)
- [Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences](https://mlsanity.com/paper/2108.07422/) (2021)
- [Neural Feature Search for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2104.02366/) (2021)
- [CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2101.08467/) (2021)
- [Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification](https://mlsanity.com/paper/2007.13890/) (2020)
- [Hetero-Center Loss for Cross-Modality Person Re-Identification](https://mlsanity.com/paper/1910.09830/) (2019)
- [Deep Learning under Privileged Information Using Heteroscedastic Dropout](https://mlsanity.com/paper/1805.11614/) (2018)
- [Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding](https://mlsanity.com/paper/1611.06638/) (2016)
- [SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2102.12137/) (2021)
- [Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification](https://mlsanity.com/paper/1905.10048/) (2019)
- [Enhancing the Discriminative Feature Learning for Visible-Thermal Cross-Modality Person Re-Identification](https://mlsanity.com/paper/1907.09659/) (2019)
- [DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination](https://mlsanity.com/paper/1909.07231/) (2019)
- [Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1709.07383/) (2017)
- [A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification](https://mlsanity.com/paper/2007.01504/) (2020)
- [Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation](https://mlsanity.com/paper/2104.05965/) (2021)
- [RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model](https://mlsanity.com/paper/2007.07452/) (2020)
- [Holistic Guidance for Occluded Person Re-Identification](https://mlsanity.com/paper/2104.06524/) (2021)
- [Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification](https://mlsanity.com/paper/2003.00213/) (2020)

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

- [Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.06825/) (2024)
- [Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2402.19026/) (2024)
- [Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data](https://mlsanity.com/paper/2211.11925/) (2022)
- [Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2307.03240/) (2023)
- [Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID](https://mlsanity.com/paper/2504.19244/) (2025)
- [MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection](https://mlsanity.com/paper/2404.18849/) (2024)
- [Learning Using Generated Privileged Information by Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.15238/) (2023)
- [Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2403.10782/) (2024)
- [Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB](https://mlsanity.com/paper/2409.11104/) (2024)
- [Mix-Modality Person Re-Identification: A New and Practical Paradigm](https://mlsanity.com/paper/2412.04719/) (2024)
- [Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data](https://mlsanity.com/paper/2305.00320/) (2023)
- [Base-Detail Feature Learning Framework for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2505.03286/) (2025)

## Abstract (arXiv)

Visible-infrared person re-identification (ReID) aims to recognize a same person of interest across a network of RGB and IR cameras. Some deep learning (DL) models have directly incorporated both modalities to discriminate persons in a joint representation space. However, this cross-modal ReID problem remains challenging due to the large domain shift in data distributions between RGB and IR modalities. % This paper introduces a novel approach for a creating intermediate virtual domain that acts as bridges between the two main domains (i.e., RGB and IR modalities) during training. This intermediate domain is considered as privileged information (PI) that is unavailable at test time, and allows formulating this cross-modal matching task as a problem in learning under privileged information (LUPI). We devised a new method to generate images between visible and infrared domains that provide additional information to train a deep ReID model through an intermediate domain adaptation. In particular, by employing color-free and multi-step triplet loss objectives during training, our method provides common feature representation spaces that are robust to large visible-infrared domain shifts. % Experimental results on challenging visible-infrared ReID datasets indicate that our proposed approach consistently improves matching accuracy, without any computational overhead at test time. The code is available at: hrefhttps://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPIhttps://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI