# Attend to the Difference: Cross-Modality Person Re-identification via Contrastive Correlation

arXiv: https://arxiv.org/abs/1910.11656 · HTML: https://mlsanity.com/paper/1910.11656/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes](https://mlsanity.com/paper/2308.11157/) (2023)
- [MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2110.11264/) (2021)
- [Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach](https://mlsanity.com/paper/2408.07500/) (2024)
- [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)
- [Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification](https://mlsanity.com/paper/2203.01675/) (2022)
- [SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification](https://mlsanity.com/paper/2603.12588/) (2026)
- [CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2208.09844/) (2022)
- [Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification](https://mlsanity.com/paper/2608.06943/) (2026)

## Abstract (arXiv)

The problem of cross-modality person re-identification has been receiving increasing attention recently, due to its practical significance. Motivated by the fact that human usually attend to the difference when they compare two similar objects, we propose a dual-path cross-modality feature learning framework which preserves intrinsic spatial strictures and attends to the difference of input cross-modality image pairs. Our framework is composed by two main components: a Dual-path Spatial-structure-preserving Common Space Network (DSCSN) and a Contrastive Correlation Network (CCN). The former embeds cross-modality images into a common 3D tensor space without losing spatial structures, while the latter extracts contrastive features by dynamically comparing input image pairs. Note that the representations generated for the input RGB and Infrared images are mutually dependant to each other. We conduct extensive experiments on two public available RGB-IR ReID datasets, SYSU-MM01 and RegDB, and our proposed method outperforms state-of-the-art algorithms by a large margin with both full and simplified evaluation modes.