A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer

Zhong, Chengyan; Qi, Guanqiu; Mazur, Neal; Banerjee, Sarbani; Malaviya, Devanshi; Hu, Gang

doi:10.3390/a14120361

Cited by 5 publications

(5 citation statements)

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“…The application of the Attention Mechanism in the Person Search task can help improve the accuracy of detection and matching [21,24,[27][28][29][30][31][32][33][34]. Chen et al [21] introduced the channel attention module into the model based on Anchor-free to express different forms of occlusion and make full use of the spatial attention module to highlight the target area of the occlusion-covered objects.…”

Section: Person Search Models Based On Attention Mechanismmentioning

confidence: 99%

“…Chen et al [21] introduced the channel attention module into the model based on Anchor-free to express different forms of occlusion and make full use of the spatial attention module to highlight the target area of the occlusion-covered objects. Zhong et al propose an enhancement to feature extraction in their work by incorporating a position-channel dual attention mechanism [33]. This mechanism aims to improve the accuracy of feature representation by selectively attending to important spatial and channel-wise information.…”

Section: Person Search Models Based On Attention Mechanismmentioning

confidence: 99%

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DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Xin,

Han,

Cui

2023

CMC

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Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect the performance of models. To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes, this paper proposes a Deformable-Attention-based Anchor-free Person Search model (DAAPS). Fully Convolutional One-Stage (FCOS), as a classic Anchor-free detector, is chosen as the model's infrastructure. The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism, applied to guide the model adaptively adjust the perceptual. The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes. The experiment proves the adaptability of the Attention mechanism to the Anchor-free model. Besides, with an improved ResNeXt+ network frame, the DAAPS model selects the Triplet-based Online Instance Matching (TOIM) Loss function to achieve a more precise end-to-end Person Search task. Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models, reaching 95.0% of mean Average Precision (mAP) and 95.6% of Top-1 on the CUHK-SYSU dataset, 48.6% of mAP and 84.7% of Top-1 on the Person Re-identification in the Wild (PRW) dataset, respectively.

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Section: Person Search Models Based On Attention Mechanismmentioning

confidence: 99%

Section: Person Search Models Based On Attention Mechanismmentioning

confidence: 99%

DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Xin,

Han,

Cui

2023

CMC

View full text Add to dashboard Cite

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“…Although stacking more layers may improve the performance of these networks, the model will be prone to gradient vanishing and explosion due to the deeper network depth. Traditional person reidentification methods based on attention [16], [17], [18], [19], [20], use attention blocks to extract features of interest. The traditional approach achieves this by stacking a large number of attention blocks, leading to a heavy model calculation task, and the previous approach makes little mention of the absence of location information and cross dimensions.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Then someone started to focus on location information to extract more fine-grained features. Zhong et al [20] improved the robustness and accuracy of feature extraction by increasing the position and channel double attention mechanism. The above methods use attention mechanisms to capture channel, spatial, and attribute information, which relieves the lack of feature discrimination to some degree.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Cross-Domain Person Re-Identification Method Based on Attention Block and Refined Clustering

Hui

et al. 2022

IEEE Access

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Most unsupervised cross-domain person re-identification methods based on clustering suffer from a lack of feature discrimination and clustering generates pseudo-labels noise, leading to a decrease in accuracy. To solve these problems, this paper proposes an unsupervised cross-domain person reidentification method based on attention block and refined clustering. Firstly, ResNet50 is selected as the backbone network, coordinate attention and triple attention are concatenated and embedded in ResNet50 to extract fine-grained features, perform feature aggregation, and mine fine-grained information. Secondly, a refined clustering strategy is proposed to achieve a coarse-to-fine clustering process by designing the measurement standards for clustering, determining its reliability, and eliminating noisy samples. Finally, the hybrid memory bank dynamically stores cluster centers and continues to update them with iterations, adapting to changes in clusters and performing invariant learning. The experimental results show that the new method designed in the paper improves the accuracy of rank-1 and mAP by 0.4% and 2.4%, respectively, on the target domain Market-1501 dataset, and improves the accuracy of rank-1 and mAP by 0.4% and 1.1%, respectively, on the target domain DukeMTMC-ReID dataset, compared with other typical methods.INDEX TERMS Attention mechanism, cross-domain person re-identification, hybrid memory bank, refined clustering strategy, unsupervised methods.

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“…In [31],the proposed remote sensing image defogging network consists of both encoding and decoding, and the dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage. It improved the definition of foggy images effectively.Zhong et al [32] integrated a dual attention network composed of position attention and channel attention into the feature extraction network, which enhanced the robustness of backbone network and achieves higher accuracy in person reidentification tasks.Guo et al [33] proposed a TBAL-Net using attention mechanism to learn fine-grained feature representation, which is an effective training framework for fine-grained class incremental learning(CIL).…”

Section: Related Work Attention Networkmentioning

confidence: 99%

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multi-Scale Feature Pyramid for Document Layout Analysis

Zhang

Shi

et al. 2022

JAIT

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Document images often contain various page components and complex logical structures, which makes document layout analysis task challenging. For most deep learning based document layout analysis methods, convolutional neural networks (CNNs) are adopted as the image feature extraction networks. In this paper, a hybrid spatial-channel attention network (HSCA-Net) is proposed to improve feature extraction capability by exerting attention mechanism to explore more salient properties within document pages. The HSCA-Net contains two modules: spatial attention module (SAM) and channel attention module (CAM). They are embedded in the multi-scale feature network by lateral attention connection. SAM extracts contextual information with learning offset in spatial dimension and CAM performs feature recalibration by focusing more on feature channels with important contents. The lateral attention connection is to incorporate SAM and CAM into multi-scale feature pyramid network and retain more of the original feature information. The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets PubLayNet, ICDAR-POD and Article Regions. The mAP on these datasets is as high as 0.940,0.939 and 0.967 respectively, which demonstrate that our HSCA-Net achieves competitive results on document layout analysis task. Text.

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A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer

Cited by 5 publications

References 50 publications

DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Unsupervised Cross-Domain Person Re-Identification Method Based on Attention Block and Refined Clustering

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multi-Scale Feature Pyramid for Document Layout Analysis

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