2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-5.1
Paper Title ADVERSARIAL SEGMENTATION LOSS FOR SKETCH COLORIZATION
Authors Samet Hicsonmez, Hacettepe University, Turkey; Nermin Samet, Emre Akbas, Middle East Technical University, Turkey; Pinar Duygulu, Hacettepe University, Turkey
SessionARS-5: Image and Video Synthesis, Rendering and Visualization
LocationArea I
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Synthesis, Rendering, and Visualization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We introduce a new method for generating color images from sketches or edge maps. Current methods either require some form of additional user-guidance or are limited to the ``paired’’ translation approach. We argue that segmentation information could provide valuable guidance for sketch colorization. To this end, we propose to leverage semantic image segmentation, as provided by a general purpose panoptic segmentation network, to create an additional adversarial loss function. Our loss function can be integrated to any baseline GAN model. Our method is not limited to datasets that contain segmentation labels, and it can be trained for ``unpaired’’ translation tasks. We show the effectiveness of our method on four different datasets spanning scene level indoor, outdoor, and children book illustration images using qualitative, quantitative and user study analysis. Our model improves its baseline up to 35 points on the FID metric. Our code and pretrained models can be found at https://github.com/giddyyupp/AdvSegLoss.