# Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification

arXiv: https://arxiv.org/abs/2206.04401 · HTML: https://mlsanity.com/paper/2206.04401/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 22 of 22)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment](https://mlsanity.com/paper/1910.05839/) (2019)
- [A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking](https://mlsanity.com/paper/1711.10378/) (2017)
- [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)
- [Differentiable Learning-to-Normalize via Switchable Normalization](https://mlsanity.com/paper/1806.10779/) (2018)
- [Hetero-Center Loss for Cross-Modality Person Re-Identification](https://mlsanity.com/paper/1910.09830/) (2019)
- [Enhancing the Discriminative Feature Learning for Visible-Thermal Cross-Modality Person Re-Identification](https://mlsanity.com/paper/1907.09659/) (2019)
- [HPILN: A feature learning framework for cross-modality person re-identification](https://mlsanity.com/paper/1906.03142/) (2019)
- [Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2006.00878/) (2020)
- [RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model](https://mlsanity.com/paper/2007.07452/) (2020)
- [An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification](https://mlsanity.com/paper/1907.06498/) (2019)
- [NAM: Normalization-based Attention Module](https://mlsanity.com/paper/2111.12419/) (2021)
- [Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification](https://mlsanity.com/paper/2003.00213/) (2020)
- [Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification](https://mlsanity.com/paper/2012.05010/) (2020)
- [Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification](https://mlsanity.com/paper/2203.01675/) (2022)

## Built on by (most cited first, 1 of 1)

- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)

## Abstract (arXiv)

In addition to considering the recognition difficulty caused by human posture and occlusion, it is also necessary to solve the modal differences caused by different imaging systems in the Visible-Thermal cross-modal person re-identification (VT-ReID) task. In this paper,we propose the Cross-modal Local Shortest Path and Global Enhancement (CM-LSP-GE) modules,a two-stream network based on joint learning of local and global features. The core idea of our paper is to use local feature alignment to solve occlusion problem, and to solve modal difference by strengthening global feature. Firstly, Attention-based two-stream ResNet network is designed to extract dual-modality features and map to a unified feature space. Then, to solve the cross-modal person pose and occlusion problems, the image are cut horizontally into several equal parts to obtain local features and the shortest path in local features between two graphs is used to achieve the fine-grained local feature alignment. Thirdly, a batch normalization enhancement module applies global features to enhance strategy, resulting in difference enhancement between different classes. The multi granularity loss fusion strategy further improves the performance of the algorithm. Finally, joint learning mechanism of local and global features is used to improve cross-modal person re-identification accuracy. The experimental results on two typical datasets show that our model is obviously superior to the most state-of-the-art methods. Especially, on SYSU-MM01 datasets, our model can achieve a gain of 2.89%and 7.96% in all search term of Rank-1 and mAP. The source code will be released soon.