# HPILN: A feature learning framework for cross-modality person re-identification

arXiv: https://arxiv.org/abs/1906.03142 · HTML: https://mlsanity.com/paper/1906.03142/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2102.12137/) (2021)
- [MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2110.11264/) (2021)
- [CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification](https://mlsanity.com/paper/2110.08994/) (2021)
- [A Versatile Framework for Multi-scene Person Re-identification](https://mlsanity.com/paper/2403.11121/) (2024)
- [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)
- [Exploiting Robust Unsupervised Video Person Re-identification](https://mlsanity.com/paper/2111.05170/) (2021)
- [On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2201.03859/) (2022)
- [Unified Batch All Triplet Loss for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2103.04607/) (2021)
- [Bridging the Distribution Gap of Visible-Infrared Person Re-identification with Modality Batch Normalization](https://mlsanity.com/paper/2103.04778/) (2021)
- [Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification](https://mlsanity.com/paper/2206.04401/) (2022)
- [Leaning Compact and Representative Features for Cross-Modality Person Re-Identification](https://mlsanity.com/paper/2103.14210/) (2021)
- [Copy and Paste method based on Pose for Re-identification](https://mlsanity.com/paper/2107.10479/) (2021)

## Abstract (arXiv)

Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera views, light brightness, etc. To meet these challenges a novel feature learning framework, HPILN, is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to improve the performance of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, which show that the proposed method outperforms all existing methods in terms of Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP).