108 citations · 303 across the 27 of their papers we have counts for
38 papers
Personalized Image Enhancement Featuring Masked Style Modeling
Satoshi Kosugi, Toshihiko Yamasaki
We address personalized image enhancement in this study, where we enhance input images for each user based on the user's preferred images. Previous methods apply the same preferred…
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter
Satoshi Kosugi, Toshihiko Yamasaki
In this study, we address local photo enhancement to improve the aesthetic quality of an input image by applying different effects to different regions. Existing photo enhancement…
Fine-Grained Image Style Transfer with Visual Transformers
Jianbo Wang, Huan Yang, Jianlong Fu +2
With the development of the convolutional neural network, image style transfer has drawn increasing attention. However, most existing approaches adopt a global feature transformati…
Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation
Koki Mukai, Soichiro Kumano, Toshihiko Yamasaki
Although Convolutional Neural Networks (CNNs) have high accuracy in image recognition, they are vulnerable to adversarial examples and out-of-distribution data, and the difference…
Detecting Deepfakes with Self-Blended Images
Kaede Shiohara, Toshihiko Yamasaki
In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from s…
Learning Where to Learn in Cross-View Self-Supervised Learning
Lang Huang, Shan You, Mingkai Zheng +3
Self-supervised learning (SSL) has made enormous progress and largely narrowed the gap with the supervised ones, where the representation learning is mainly guided by a projection…