Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style image to stylize an input video.
We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function.
This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method.
In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors.
Ranked #123 on Object Detection on COCO test-dev
We further validate the effectiveness of our proposal in another task, namely, "labels to photo" image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases.
Ranked #100 on Object Detection on COCO minival
To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers.
The style was noticeable in other characters of the same illustrator in different books as well.