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    Phase Consistent Ecological Domain Adaptation

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    Type
    Conference Paper
    Authors
    Yang, Yanchao
    Alzahrani, Majed A. cc
    Sundaramoorthi, Ganesh cc
    Soatto, Stefano
    KAUST Department
    Applied Mathematics & Computational Sci
    Applied Mathematics and Computational Science Program
    Computational Vision Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2020-08-05
    Preprint Posting Date
    2020-04-10
    Online Publication Date
    2020-08-05
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/662571
    
    Metadata
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    Abstract
    We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a different domain, a problem known as unsupervised domain adaptation. We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. This restricts the set of possible learned maps, while enabling enough flexibility to transfer semantic information. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor. It is implemented using a deep neural network that scores the likelihood of each possible segmentation given a single un-annotated image. Incorporating these two priors in a standard domain adaptation framework improves performance across the board in the most common unsupervised domain adaptation benchmarks for semantic segmentation.
    Citation
    Yang, Y., Lao, D., Sundaramoorthi, G., & Soatto, S. (2020). Phase Consistent Ecological Domain Adaptation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr42600.2020.00903
    Sponsors
    Research supported by ARO W911NF-17-1-0304 and ONR N00014-19-1-2066. Dong Lao is supported by KAUST through the VCC Center Competitive Funding
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    ISBN
    978-1-7281-7169-2
    DOI
    10.1109/CVPR42600.2020.00903
    arXiv
    2004.04923
    Additional Links
    https://ieeexplore.ieee.org/document/9157388/
    https://ieeexplore.ieee.org/document/9157388/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9157388
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR42600.2020.00903
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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