Learning Foreground-Background Segmentation from Improved Layered GANs

Yang Yu, Hakan Bilen, Qiran Zou, Wing Yin Cheung, Xianyang Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to automatically synthesize paired photo-realistic images and segmentation masks for the use of training a foreground-background segmentation network. In particular, we learn a generative adversarial network that decomposes an image into foreground and background layers, and avoid trivial decompositions by maximizing mutual information between generated images and latent variables. The improved layered GANs can synthesize higher quality datasets from which segmentation networks of higher performance can be learned. Moreover, the segmentation networks are employed to stabilize the training of layered GANs in return, which are further alternately trained with Layered GANs. Experiments on a variety of single-object datasets show that our method achieves competitive generation quality and segmentation performance compared to related methods.
Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
PublisherInstitute of Electrical and Electronics Engineers
Pages366-375
Number of pages10
ISBN (Electronic)978-1-6654-0915-5
ISBN (Print)978-1-6654-0916-2
DOIs
Publication statusPublished - 15 Feb 2022
Event2022 IEEE Winter Conference on Applications of Computer Vision - Waikoloa, United States
Duration: 4 Jan 20228 Jan 2022
https://wacv2022.thecvf.com/

Publication series

NameIEEE Workshop on Applications of Computer Vision (WACV)
PublisherIEEE
ISSN (Print)2472-6737
ISSN (Electronic)2642-9381

Conference

Conference2022 IEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2022
Country/TerritoryUnited States
CityWaikoloa
Period4/01/228/01/22
Internet address

Keywords / Materials (for Non-textual outputs)

  • Machine learning
  • Computer Vision
  • Unsupervised Learning
  • Generative adversarial network

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