81 citations · 122 across the 6 of their papers we have counts for
11 papers
SwinIR: Image Restoration Using Swin Transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun +3
Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). Whil…
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
Jingyun Liang, Guolei Sun, Kai Zhang +2
Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for r…
Boosting Few-shot Semantic Segmentation with Transformers
Guolei Sun, Yun Liu, Jingyun Liang +1
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentati…
CompositeTasking: Understanding Images by Spatial Composition of Tasks
Nikola Popovic, Danda Pani Paudel, Thomas Probst +2
We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distribu…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
Guolei Sun, Wenguan Wang, Jifeng Dai +1
This paper studies the problem of learning semantic segmentation from image-level supervision only. Current popular solutions leverage object localization maps from classifiers as…