Margin-Based Modal Adaptive Learning for Visible-Infrared Person Re-Identification

Zhao, Qianqian; Wu, Hanxiao; Zhu, Jianqing

doi:10.3390/s23031426

Cited by 3 publications

(3 citation statements)

References 71 publications

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“…Cross-modality person ReID [5][6][7][8][9] retrieves RGB pedestrian images from infrared (IR) pedestrian images and vice versa. It not only inherits the challenges of unimodality person ReID, such as the variations in postures, illumination, and camera view, but also possesses a large discrepancy between IR modality and RGB modality.…”

Section: Introductionmentioning

confidence: 99%

Cross-Modality Person Re-Identification via Local Paired Graph Attention Network

Zhou

Dong

Zhang

et al. 2023

Sensors

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Cross-modality person re-identification (ReID) aims at searching a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Recently, some approaches have constructed a graph to learn the relevance of pedestrian images of distinct modalities to narrow the gap between IR modality and RGB modality, but they omit the correlation between IR image and RGB image pairs. In this paper, we propose a novel graph model called Local Paired Graph Attention Network (LPGAT). It uses the paired local features of pedestrian images from different modalities to build the nodes of the graph. For accurate propagation of information among the nodes of the graph, we propose a contextual attention coefficient that leverages distance information to regulate the process of updating the nodes of the graph. Furthermore, we put forward Cross-Center Contrastive Learning (C3L) to constrain how far local features are from their heterogeneous centers, which is beneficial for learning the completed distance metric. We conduct experiments on the RegDB and SYSU-MM01 datasets to validate the feasibility of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Cross-Modality Person Re-Identification via Local Paired Graph Attention Network

Zhou

Dong

Zhang

et al. 2023

Sensors

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“…Nowadays, there are cameras that automatically switch to infrared mode at night to improve the capture of person characteristic information at night. However, the common method in cross-modal person re-identification is visible-infrared person re-identification (VI-REID) [2][3] , as shown in Figure . 1. However, due to the influence of factors such as different postures of persons, occlusion of person parts, clutter background, and inter-modal differences, VI-REID task is not only faced with the differences between person images within a single modality.…”

Section: Introductionmentioning

confidence: 99%

Mask-guided two-stream attention network with modality alignment for infrared-visible person re-identification

Gou,

Zhang,

Lan

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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In the existing person re-identification research, most of the studies are based on the person re-identification of visible light images, and the illumination conditions can affect the experimental results. Therefore, cross-modal person reidentification research has emerged, and the most commonly used method is visible-infrared person re-identification. Due to different cameras, different camera angles, different scenes, and other factors, cross-modal person reidentification faces great challenges of cross-modal differences and intra-class variations. Therefore, Mask-guided Twostream Attention Network (MTAN) with modality alignment for infrared-visible person re-identification is proposed in this paper. First, the mask-guided two-stream feature extraction network is proposed to extract shape-aware modality features. Then, the modality alignment module is designed to eliminate cross-modal feature distribution bias. Overall, the proposed method combines the mask-guided two-stream feature extraction network, modality alignment module and the loss functions, to effectively extract modality-aware distinguishable features and reduce cross-modal distribution bias.

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“…Person re-identification (Re-ID) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] is a complex computer vision task that focuses on matching individuals across non-overlapping camera views. The main objective is to associate images or videos of the same person while maintaining a low recall rate, thereby reducing the likelihood of incorrect matches.…”

Section: Introductionmentioning

confidence: 99%

Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification

Jiang,

Xiao,

Wang

et al. 2023

Sensors

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With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP.

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Margin-Based Modal Adaptive Learning for Visible-Infrared Person Re-Identification

Cited by 3 publications

References 71 publications

Cross-Modality Person Re-Identification via Local Paired Graph Attention Network

Cross-Modality Person Re-Identification via Local Paired Graph Attention Network

Mask-guided two-stream attention network with modality alignment for infrared-visible person re-identification

Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification

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