# Homogeneous and Heterogeneous Relational Graph for Visible-infrared Person Re-identification

arXiv: https://arxiv.org/abs/2109.08811 · HTML: https://mlsanity.com/paper/2109.08811/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [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)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [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)
- [AlignedReID: Surpassing Human-Level Performance in Person Re-Identification](https://mlsanity.com/paper/1711.08184/) (2017)
- [Learning Context Graph for Person Search](https://mlsanity.com/paper/1904.01830/) (2019)
- [Neural Feature Search for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2104.02366/) (2021)
- [Adaptive Graph Representation Learning for Video Person Re-identification](https://mlsanity.com/paper/1909.02240/) (2019)
- [Relation Network for Person Re-identification](https://mlsanity.com/paper/1911.09318/) (2019)
- [Deep-Person: Learning Discriminative Deep Features for Person Re-Identification](https://mlsanity.com/paper/1711.10658/) (2017)
- [Pose-driven Attention-guided Image Generation for Person Re-Identification](https://mlsanity.com/paper/2104.13773/) (2021)
- [Deep Attention Aware Feature Learning for Person Re-Identification](https://mlsanity.com/paper/2003.00517/) (2020)
- [G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2106.07853/) (2021)
- [Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification](https://mlsanity.com/paper/2104.11539/) (2021)

## Built on by (most cited first, 3 of 3)

- [DiCo: Disentangled Concept Representation for Text-to-image Person Re-identification](https://mlsanity.com/paper/2601.10053/) (2026)
- [SCMM: Calibrating Cross-modal Representations for Text-Based Person Search](https://mlsanity.com/paper/2304.02278/) (2023)
- [TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation](https://mlsanity.com/paper/2607.25471/) (2026)

## Abstract (arXiv)

Visible-infrared person re-identification (VI Re-ID) aims to match person images between the visible and infrared modalities. Existing VI Re-ID methods mainly focus on extracting homogeneous structural relationships in an image, i.e. the relations between local features, while ignoring the heterogeneous correlation of local features in different modalities. The heterogeneous structured relationship is crucial to learn effective identity representations and perform cross-modality matching. In this paper, we model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation with the modality-specific graph of visible and infrared modality. First, the homogeneous structured graph (HOSG) mines one-vs.-rest relation between an arbitrary node (local feature) and all the rest nodes within a visible or infrared image to learn effective identity representation. Second, to find cross-modality identity-consistent correspondence, the heterogeneous graph alignment module (HGAM) further measures the relational edge strength between local node features of two modalities with routing search way. Third, we propose the cross-modality cross-correlation (CMCC) loss to extract the modality invariance of feature representations of visible and infrared graphs. CMCC computes the mutual information between modalities and expels semantic redundancy. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that our method outperforms state-of-the-arts with a gain of 13.73% and 9.45% Rank1/mAP. The code is available at https://github.com/fegnyujian/Homogeneous-and-Heterogeneous-Relational-Graph.