Search Results for author: Jingyun Liang

Found 7 papers, 5 papers with code

SwinIR: Image Restoration Using Swin Transformer

2 code implementations23 Aug 2021 Jingyun Liang, JieZhang Cao, Guolei Sun, Kai Zhang, Luc van Gool, Radu Timofte

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Color Image Denoising Image Denoising +4

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

1 code implementation ICCV 2021 Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc van Gool, Radu Timofte

More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously.

Image Super-Resolution

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution

1 code implementation ICCV 2021 Jingyun Liang, Guolei Sun, Kai Zhang, Luc van Gool, Radu Timofte

Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.

Affine Transformation Image Super-Resolution

Boosting Few-shot Semantic Segmentation with Transformers

no code implementations4 Aug 2021 Guolei Sun, Yun Liu, Jingyun Liang, Luc van Gool

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 segmentation has attracted lots of research attention.

Few-Shot Semantic Segmentation Semantic Segmentation

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

1 code implementation ICCV 2021 Kai Zhang, Jingyun Liang, Luc van Gool, Radu Timofte

It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.

Image Super-Resolution

Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition

no code implementations22 Jul 2019 Chengyu Guo, Jingyun Liang, Geng Zhan, Zhong Liu, Matti Pietikäinen, Li Liu

It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition.

Micro-Expression Recognition

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