2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSMR-3.8
Paper Title DUAL-CONSISTENCY SELF-TRAINING FOR UNSUPERVISED DOMAIN ADAPTATION
Authors Jie Wang, Chaoliang Zhong, Cheng Feng, Jun Sun, Fujitsu R&D Center, Co., LTD, China; Masaru Ide, Yasuto Yokota, Fujitsu Laboratories, Japan
SessionSMR-3: Image and Video Representation
LocationArea F
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Sensing, Modeling, and Representation: Image & video representation
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Abstract Unsupervised domain adaptation (UDA) is a challenging task characterized by unlabeled target data with domain discrepancy to labeled source data. Many methods have been proposed to learn domain invariant features by marginal distribution alignment, but they ignore the intrinsic structure within target domain, which may lead to insufficient or false alignment. Class-level alignment has been demonstrated to align the features of the same class between source and target domains. These methods rely extensively on the accuracy of predicted pseudo-labels for target data. Here, we develop a novel self-training method that focuses more on accurate pseudo-labels via a dual-consistency strategy involving modelling the intrinsic structure of the target domain. The proposed dual-consistency strategy first improves the accuracy of pseudo-labels through voting consistency, and then reduces the negative effects of incorrect predictions through structure consistency with the relationship of intrinsic structures across domains. Our method has achieved comparable performance to the state-of-the-arts on three standard UDA benchmarks.