Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation

Li, Yu-Jhe; Lin, Ci-Siang; Lin, Yan-Bo; Wang, Yu-Chiang Frank

doi:10.1109/iccv.2019.00801

Cited by 177 publications

(94 citation statements)

References 49 publications

(85 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%

“…Despite the significant success, domain adaptive person ReID is still a challenging task and there remain several open issues unexplored. Firstly, previous works commonly conduct the knowledge transfer in two stages, i.e., first transfer labeled images to the target domain with Generative Adversarial Networks (GANs), then train the ReID model [4,14,23,27] using transferred images. However, GANs could be hard to tune.The image generation is also challenging and sensitive to various factors like backgrounds, lighting, etc.. Secondly, person ReID models for the unlabeled target domain can be optimized by predicted labels.…”

Section: Figure 1: Illustration Of Proposed Coupling Optimization Metmentioning

confidence: 99%

“…Recently, many works have been proposed for domain adaptive person ReID task from different aspects. Some works targeting to narrow domain gaps in image space by GANs [4,14,23,27,40,59]. For example, Liu et al [27] propose an adaptive transfer network to effectively transfer images from label domain to unlabeled domain.…”

Section: Domain Adaptive Person Re-identificationmentioning

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%

Section: Figure 1: Illustration Of Proposed Coupling Optimization Metmentioning

confidence: 99%

Section: Domain Adaptive Person Re-identificationmentioning

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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“…Another frequently-used strategy is the cross domain person re-id solution. They are either leverage the feature gap or transform the image style to bridge the distribution gap [2], [10], [23]. Li et al [10] proposed a pose disentanglement and adaptation network aiming at learning deep image representation with pose and domain information properly disentangled, and it can perform pose disentanglement across domains without supervision in identities.…”

Section: B Cross Domain Person Re-identificationmentioning

confidence: 99%

“…They are either leverage the feature gap or transform the image style to bridge the distribution gap [2], [10], [23]. Li et al [10] proposed a pose disentanglement and adaptation network aiming at learning deep image representation with pose and domain information properly disentangled, and it can perform pose disentanglement across domains without supervision in identities. Chen et al [2] proposed an instance-guided context rendering scheme for cross-domain person re-identification, which transfer the source person identities into diverse target domain contexts to enable supervised re-id model learning in the unlabeled target domain.…”

Section: B Cross Domain Person Re-identificationmentioning

confidence: 99%

Deep Multi-Task Transfer Network for Cross Domain Person Re-Identification

Wang

2020

IEEE Access

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As a prominent application of surveillance video analysis, person re-identification attracts much more research attention recently. Existing person re-identification models often focus on supervision by the pedestrian identity annotation, while it has limited scalability in realistic. Though several unsupervised person re-identification researches pay attention to solve this problem, they are either clustering based or cross domain based approaches, where a conventional assumption of them is the identity number of the target dataset is acknowledged. To relax this hypothesis, we propose a Deep Multi-task Transfer Network (DMTNet) for cross domain person re-identification, which conduct classification, attribute attention and identification task between source and target domains. There are three main novelties in DMTNet, including clustering number estimating algorithm to learn prior knowledge from source data to estimate the identity number, attribute attention importance learning rather than directly utilizing attribute information, and a multi-task transfer learning mechanism to transfer specific tasks cross domains. To prove the superiority of our DMTNet, we implement several compared experiments on DukeMTMC-reID and Market-1501 datasets, which results show the advancement of our network. Moreover, the discussions for different modules also point out the significance of the specific tasks. INDEX TERMS Cross domain person re-identification, multi-task transfer, attribute attention, identity number estimating.

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