A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification

Qi, Lei; Wang, Lei; Huo, Jing; Zhou, Luping; Shi, Yinghuan; Gao, Yang

doi:10.1109/iccv.2019.00817

Cited by 149 publications

(63 citation statements)

References 23 publications

(47 reference statements)

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“…Comparison on domain adaptive person ReID on Market-1501 and DukeMTMC-reID. Proposed DIM+GLO model is compared with recent state-of-the-art methods including TAUDL † [21], UTAL † [22], MAR [46], PAUL [45], CASCL [42], UDA [33], GPP [61], HHL [59], ECN [60], ATNet [27], CR_GAN [4], SSG ‡ [7], DA_2S [14], CAL * † [32], PDA-Net [23], and PAST ‡ [49]. The comparison results are summarized in Table 3.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

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Domain Adaptive Person Re-Identification via Coupling Optimization

Liu

Zhang

2020

Proceedings of the 28th ACM International Conference on Multimedia

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Domain adaptive person Re-Identification (ReID) is challenging owing to the domain gap and shortage of annotations on target scenarios. To handle those two challenges, this paper proposes a coupling optimization method including the Domain-Invariant Mapping (DIM) method and the Global-Local distance Optimization (GLO), respectively. Different from previous methods that transfer knowledge in two stages, the DIM achieves a more efficient one-stage knowledge transfer by mapping images in labeled and unlabeled datasets to a shared feature space. GLO is designed to train the ReID model with unsupervised setting on the target domain. Instead of relying on existing optimization strategies designed for supervised training, GLO involves more images in distance optimization, and achieves better robustness to noisy label prediction. GLO also integrates distance optimizations in both the global dataset and local training batch, thus exhibits better training efficiency. Extensive experiments on three large-scale datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, show that our coupling optimization outperforms state-of-the-art methods by a large margin. Our method also works well in unsupervised training, and even outperforms several recent domain adaptive methods.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Some works adopt additional cues to boost performance [7,21,22,32,49]. For example, Qi et al [32] use temporal information and Li et al [21,22] adopt tracklet information to predict more precise labels on unlabeled datasets. Fu et al [7] and Zhang et al [49] use local features to improve the performance.…”

Section: Domain Adaptive Person Re-identificationmentioning

confidence: 99%

Domain Adaptive Person Re-Identification via Coupling Optimization

Liu

Zhang

2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…We compare our proposed A 2 G framework with unsupervised, unsupervised domain adaptation, and attribute auxiliary weakly-supervised methods on four cross-dataset person re-ID tasks: Duke-to-Market, Market-to-Duke, MSMTto-Duke, and MSMT-to-Market. We compare three types of approaches, including unsupervised learning methods: PUAL [45], BUC [16], SSL [10], HCT [46], D-MMD [49], CSE [10], and TAUDL [47], transfer learning based methods: SPGAN [20], HHL [22], CFSM [48], ENC [15], UDATP [25], UCDA-CCE [50], PDA-Net [51], PCB-PAST [8], SSG [7], MMCL [24], DG-NET++ [52], B-SNR+GDS-H [53], DGNET [3], OG-Net [54], AE [17], and AD-Cluster [55], and attribute auxiliary weakly supervised method: TJ-AIDL [26].…”

Section: Comparison With the State-of-the-artsmentioning

confidence: 99%

Graph Neural Network Based Attribute Auxiliary Structured Grouping for Person Re-Identification

Tang

Gao²,

Chen

et al. 2024

IEEE Access

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Recently, person re-identification (re-ID) with weakly labeled or unlabeled data has drawn much attention in open-set and cross-domain re-ID systems especially for the attribute auxiliary weakly supervised person re-ID. Although state-of-the-art clustering-based methods have achieved good performance, the pseudo labels generated through clustering are often low-quality and noisy. To address this problem, we propose a graph neural network based Attribute Auxiliary structured Grouping (A 2 G) to improve the confidence of the pseudo labels. Different from the existing clustering-based approaches that only utilize the similarity in feature space, we learn the feature representation from the similarities in both attribute space and feature space by graph learning on the pedestrian attribute graph. Specifically, we first utilize the pair-wise attribute similarity to represent the relation between two pedestrians to construct a pedestrian attribute graph. Furthermore, we aggregate the features from their neighborhood on a pedestrian attribute graph by the graph neural network, which would make the attribute similar pairs closer and simultaneously take the dissimilar pairs further in the feature space. Finally, to avoid the over-confidence of the hard pseudo labels, we regularize the learning of the embedding model with the smoothed pseudo label (SPL) in the optimization of the feature embedding network. We conduct extensive experiments on several person re-ID datasets to validate the efficacy of our proposed method. The results demonstrate that our technique is effective and promising for person re-ID tasks.INDEX TERMS Unsupervised person re-identification, attribute-auxiliary structured grouping, graph neural network.

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“…And Liu et al [26] used GAN to generate images with the target camera domain style. Qi et al [254] developed an unsupervised online in-batch triplet generation method to explore the discriminative information in the target domain. Then, an alternating optimization algorithm [259] is designed to jointly solve the cross-view and cross-domain problems.…”

Section: ) Data Enhancementmentioning

confidence: 99%

A Comprehensive Overview of Person Re-Identification Approaches

Wang

Zhao

et al. 2020

IEEE Access

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Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference. INDEX TERMSComputer vision, convolutional neural networks, distance learning, feature extraction, person re-identification.

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A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification

Cited by 149 publications

References 23 publications

Domain Adaptive Person Re-Identification via Coupling Optimization

Domain Adaptive Person Re-Identification via Coupling Optimization

Graph Neural Network Based Attribute Auxiliary Structured Grouping for Person Re-Identification

A Comprehensive Overview of Person Re-Identification Approaches

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