Generative Modelling of Semantic Segmentation Data in the Fashion Domain

Marie Korneliusson, John Martinsson, Olof Mogren

ICCVW 2019 pp. 3169-3172

doi:10.1109/ICCVW.2019.00391 /iccvw/2019/korneliusson2019iccvw-generative/

Abstract

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.

ICCVW Semantic Scholar

Cite

Text

Korneliusson et al. "Generative Modelling of Semantic Segmentation Data in the Fashion Domain." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00391

Markdown

[Korneliusson et al. "Generative Modelling of Semantic Segmentation Data in the Fashion Domain." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/korneliusson2019iccvw-generative/) doi:10.1109/ICCVW.2019.00391

BibTeX

@inproceedings{korneliusson2019iccvw-generative,
  title     = {{Generative Modelling of Semantic Segmentation Data in the Fashion Domain}},
  author    = {Korneliusson, Marie and Martinsson, John and Mogren, Olof},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {3169-3172},
  doi       = {10.1109/ICCVW.2019.00391},
  url       = {https://mlanthology.org/iccvw/2019/korneliusson2019iccvw-generative/}
}