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20182022
most citedPixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing

7 citations · 11 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CV20224 cited

Background Mixup Data Augmentation for Hand and Object-in-Contact Detection

Koya Tango, Takehiko Ohkawa, Ryosuke Furuta +1

Detecting the positions of human hands and objects-in-contact (hand-object detection) in each video frame is vital for understanding human activities from videos. For training an o…

cs.CV2021

Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks

Yugo Shimizu, Ryosuke Furuta, Delong Ouyang +3

Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have…

cs.CV20197 cited

PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing

Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has b…

cs.CV2018

Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has b…

cs.CV2018

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki +1

Can we detect common objects in a variety of image domains without instance-level annotations? In this paper, we present a framework for a novel task, cross-domain weakly supervise…