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Generative Modelling of Semantic Segmentation Data in the Fashion Domain
Chalmers University of Technology, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-5032-4367
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-9567-2218
2019 (English)In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019, p. 3169-3172Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we propose a method to generatively model the joint distribution of images and corresponding semantic segmentation masks using generative adversarial networks. We extend the Style-GAN architecture by iteratively growing the network during training, to add new output channels that model the semantic segmentation masks. We train the proposed method on a large dataset of fashion images and our experimental evaluation shows that the model produces samples that are coherent and plausible with semantic segmentation masks that closely match the semantics in the image.

Place, publisher, year, edition, pages
2019. p. 3169-3172
Keywords [en]
image segmentation, generative modelling, semantic segmentation data, fashion domain, corresponding semantic segmentation masks, generative adversarial networks, Style-GAN architecture, fashion images, semantics, Generators, Training, Gallium nitride, Computer vision, deep learning, artificial neural networks, semantic segmentations, clothing parsing
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-51880DOI: 10.1109/ICCVW.2019.00391OAI: oai:DiVA.org:ri-51880DiVA, id: diva2:1519212
Conference
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Available from: 2021-01-18 Created: 2021-01-18 Last updated: 2023-06-02Bibliographically approved

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Martinsson, JohnMogren, Olof

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  • apa
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  • de-DE
  • en-GB
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  • nn-NO
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Output format
  • html
  • text
  • asciidoc
  • rtf