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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 309–324Cite as

Domain Adaptive Object Detection via Asymmetric Tri-Way Faster-RCNN

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12369)

Abstract

Conventional object detection models inevitably encounter a performance drop as the domain disparity exists. Unsupervised domain adaptive object detection is proposed recently to reduce the disparity between domains, where the source domain is label-rich while the target domain is label-agnostic. The existing models follow a parameter shared siamese structure for adversarial domain alignment, which, however, easily leads to the collapse and out-of-control risk of the source domain and brings negative impact to feature adaption. The main reason is that the labeling unfairness (asymmetry) between source and target makes the parameter sharing mechanism unable to adapt. Therefore, in order to avoid the source domain collapse risk caused by parameter sharing, we propose an asymmetric tri-way Faster-RCNN (ATF) for domain adaptive object detection. Our ATF model has two distinct merits: 1) A ancillary net supervised by source label is deployed to learn ancillary target features and simultaneously preserve the discrimination of source domain, which enhances the structural discrimination (object classification vs. bounding box regression) of domain alignment. 2) The asymmetric structure consisting of a chief net and an independent ancillary net essentially overcomes the parameter sharing aroused source risk collapse. The adaption safety of the proposed ATF detector is guaranteed. Extensive experiments on a number of datasets, including Cityscapes, Foggy-cityscapes, KITTI, Sim10k, Pascal VOC, Clipart and Watercolor, demonstrate the SOTA performance of our method.

Keywords

  • Object detection
  • Transfer learning
  • Deep learning

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Acknowledgement

This work was supported by the National Science Fund of China under Grants (61771079) and Chongqing Youth Talent Program.

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Authors and Affiliations

  1. Learning Intelligence and Vision Essential (LiVE) Group, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Zhenwei He & Lei Zhang

Authors
  1. Zhenwei He
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  2. Lei Zhang
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Correspondence to Lei Zhang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Prof. Dr. Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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He, Z., Zhang, L. (2020). Domain Adaptive Object Detection via Asymmetric Tri-Way Faster-RCNN. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-58586-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58585-3

  • Online ISBN: 978-3-030-58586-0

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