# RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification

arXiv: https://arxiv.org/abs/2411.01225 · HTML: https://mlsanity.com/paper/2411.01225/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 2 Nov 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [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)
- [Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14481/) (2023)
- [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)
- [Person Re-identification Method Based on Color Attack and Joint Defence](https://mlsanity.com/paper/2111.09571/) (2021)
- [Neural Feature Search for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2104.02366/) (2021)
- [Cross-Modality Perturbation Synergy Attack for Person Re-identification](https://mlsanity.com/paper/2401.10090/) (2024)
- [CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2101.08467/) (2021)
- [Dynamic Prototype Mask for Occluded Person Re-Identification](https://mlsanity.com/paper/2207.09046/) (2022)
- [Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.06825/) (2024)
- [PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification](https://mlsanity.com/paper/2408.16684/) (2024)
- [MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14626/) (2023)
- [Exploring Invariant Representation for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2302.00884/) (2023)
- [Graphical Representation for Heterogeneous Face Recognition](https://mlsanity.com/paper/1503.00488/) (2015)
- [Spectral Aware Softmax for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2302.01512/) (2023)

## Built on by (most cited first, 16 of 16)

- [MDReID: Modality-Decoupled Learning for Any-to-Any Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2510.23301/) (2025)
- [Motion-Aware Caching for Efficient Autoregressive Video Generation](https://mlsanity.com/paper/2605.01725/) (2026)
- [GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2510.22268/) (2025)
- [FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks](https://mlsanity.com/paper/2504.09451/) (2025)
- [MFEN:Multi-Frequency Expert Network for Visible-Infrared Person Re-ID](https://mlsanity.com/paper/2606.12051/) (2026)
- [Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO](https://mlsanity.com/paper/2607.11581/) (2026)
- [ShapeSpeak: Body Shape-Aware Textual Alignment for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2504.18025/) (2025)
- [Modality-Aware Bias Mitigation and Invariance Learning for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2512.07760/) (2025)
- [Image-Text Knowledge Modeling for Unsupervised Multi-Scenario Person Re-Identification](https://mlsanity.com/paper/2601.11243/) (2026)
- [Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2601.21405/) (2026)
- [ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization](https://mlsanity.com/paper/2605.12159/) (2026)
- [FUSE: Frequency-domain Unification and Spectral Energy Alignment for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2606.20044/) (2026)
- [Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification](https://mlsanity.com/paper/2607.14821/) (2026)
- [Current Injection Spiking Neural Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2607.19879/) (2026)
- [Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification](https://mlsanity.com/paper/2608.06943/) (2026)
- [Prototype Matters: Modality-unified Prototype Self-distillation for Unsupervised Visible-infrared Person Re-identification](https://mlsanity.com/paper/2609.11514/) (2026)

## Abstract (arXiv)

This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modality discrepancy mainly comes from diverse linear transformations acting on the surface of different materials. From this view, we unify all data augmentation strategies for cross-spectral re-identification by mimicking such local linear transformations and categorizing them into moderate transformation and radical transformation. By extending the observation, we propose a Random Linear Enhancement (RLE) strategy which includes Moderate Random Linear Enhancement (MRLE) and Radical Random Linear Enhancement (RRLE) to push the boundaries of both types of transformation. Moderate Random Linear Enhancement is designed to provide diverse image transformations that satisfy the original linear correlations under constrained conditions, whereas Radical Random Linear Enhancement seeks to generate local linear transformations directly without relying on external information. The experimental results not only demonstrate the superiority and effectiveness of RLE but also confirm its great potential as a general-purpose data augmentation for cross-spectral re-identification. The code is available at textcolormagentaurlhttps://github.com/stone96123/RLE.