# SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification

arXiv: https://arxiv.org/abs/2102.12137 · HTML: https://mlsanity.com/paper/2102.12137/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [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)
- [Person Re-identification with Metric Learning using Privileged Information](https://mlsanity.com/paper/1904.05005/) (2019)
- [Enhancing the Discriminative Feature Learning for Visible-Thermal Cross-Modality Person Re-Identification](https://mlsanity.com/paper/1907.09659/) (2019)
- [3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification](https://mlsanity.com/paper/1812.10222/) (2018)
- [HPILN: A feature learning framework for cross-modality person re-identification](https://mlsanity.com/paper/1906.03142/) (2019)
- [Cross-Entropy Adversarial View Adaptation for Person Re-identification](https://mlsanity.com/paper/1904.01755/) (2019)
- [Few-Shot Deep Adversarial Learning for Video-based Person Re-identification](https://mlsanity.com/paper/1903.12395/) (2019)
- [View Confusion Feature Learning for Person Re-identification](https://mlsanity.com/paper/1910.03849/) (2019)
- [An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification](https://mlsanity.com/paper/1907.06498/) (2019)
- [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, 20 of 20)

- [MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning](https://mlsanity.com/paper/2304.08028/) (2023)
- [Robust Multimodal Learning via Representation Decoupling](https://mlsanity.com/paper/2407.04458/) (2024)
- [Visible-Infrared Person Re-Identification Using Privileged Intermediate Information](https://mlsanity.com/paper/2209.09348/) (2022)
- [Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification](https://mlsanity.com/paper/2104.11539/) (2021)
- [Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2307.03240/) (2023)
- [Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification](https://mlsanity.com/paper/2203.01675/) (2022)
- [One-stage Modality Distillation for Incomplete Multimodal Learning](https://mlsanity.com/paper/2309.08204/) (2023)
- [Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2204.04842/) (2022)
- [Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment](https://mlsanity.com/paper/2404.06683/) (2024)
- [Spectral Enhancement and Pseudo-Anchor Guidance for Infrared-Visible Person Re-Identification](https://mlsanity.com/paper/2412.19111/) (2024)
- [MFEN:Multi-Frequency Expert Network for Visible-Infrared Person Re-ID](https://mlsanity.com/paper/2606.12051/) (2026)
- [YYDS: Visible-Infrared Person Re-Identification with Coarse Descriptions](https://mlsanity.com/paper/2403.04183/) (2024)
- [Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2403.10782/) (2024)
- [CLIP4VI-ReID: Learning Modality-shared Representations via CLIP Semantic Bridge for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2511.10309/) (2025)
- [Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification](https://mlsanity.com/paper/2310.08026/) (2023)
- [Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2312.07021/) (2023)
- [Dynamic Identity-Guided Attention Network for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2405.12713/) (2024)
- [Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2511.16184/) (2025)
- [FGAA-FPN: Foreground-Guided Angle-Aware Feature Pyramid Network for Oriented Object Detection](https://mlsanity.com/paper/2602.10710/) (2026)
- [Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2608.16015/) (2026)

## Abstract (arXiv)

Visible-Infrared person re-identification (VI-ReID) is a challenging matching problem due to large modality varitions between visible and infrared images. Existing approaches usually bridge the modality gap with only feature-level constraints, ignoring pixel-level variations. Some methods employ GAN to generate style-consistent images, but it destroys the structure information and incurs a considerable level of noise. In this paper, we explicitly consider these challenges and formulate a novel spectrum-aware feature augementation network named SFANet for cross-modality matching problem. Specifically, we put forward to employ grayscale-spectrum images to fully replace RGB images for feature learning. Learning with the grayscale-spectrum images, our model can apparently reduce modality discrepancy and detect inner structure relations across the different modalities, making it robust to color variations. In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks, which preserve the spatial structure information of features. Additionally, a bi-directional tri-constrained top-push ranking loss (BTTR) is embedded in the proposed network to improve the discriminability, which efficiently further boosts the matching accuracy. Meanwhile, we further introduce an effective dual-linear with batch normalization ID embedding method to model the identity-specific information and assits BTTR loss in magnitude stabilizing. On SYSU-MM01 and RegDB datasets, we conducted extensively experiments to demonstrate that our proposed framework contributes indispensably and achieves a very competitive VI-ReID performance.