Sorry, you need to enable JavaScript to visit this website.

ADVERSARIAL SEGMENTATION LOSS FOR SKETCH COLORIZATION

Citation Author(s):
Samet Hicsonmez, Nermin Samet, Emre Akbas, Pinar Duygulu
Submitted by:
Samet Hicsonmez
Last updated:
2 October 2021 - 3:47am
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Samet Hicsonmez
Paper Code:
ICIP-1080

Abstract 

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.

up
0 users have voted:

Dataset Files

Paper_1080_poster.pdf

(11)