23 citations · 59 across the 7 of their papers we have counts for
6 papers · 1 filter
w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution
Bo Wang, Lei Wang, Junyang Chen +3
Deep learning-based medical image segmentation technology aims at automatic recognizing and annotating objects on the medical image. Non-local attention and feature learning by mul…
Efficient Medical Image Segmentation with Intermediate Supervision Mechanism
Di Yuan, Junyang Chen, Zhenghua Xu +3
Because the expansion path of U-Net may ignore the characteristics of small targets, intermediate supervision mechanism is proposed. The original mask is also entered into the netw…
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
Chang Qi, Junyang Chen, Guizhi Xu +3
Recently deep learning methods, in particular, convolutional neural networks (CNNs), have led to a massive breakthrough in the range of computer vision. Also, the large-scale annot…
Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network
Cong Wang, Yutong Wu, Zhixun Su +1
In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. I…
DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal
Cong Wang, Xiaoying Xing, Zhixun Su +1
Rain removal is an important but challenging computer vision task as rain streaks can severely degrade the visibility of images that may make other visions or multimedia tasks fail…
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +43
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world settin…