Search Results for author: Yawei Li

Found 27 papers, 20 papers with code

Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

1 code implementation1 Mar 2023 Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, Luc van Gool

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.

Image Restoration

Reference-based Image Super-Resolution with Deformable Attention Transformer

1 code implementation25 Jul 2022 JieZhang Cao, Jingyun Liang, Kai Zhang, Yawei Li, Yulun Zhang, Wenguan Wang, Luc van Gool

Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images.

Image Super-Resolution

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

Revisiting Random Channel Pruning for Neural Network Compression

1 code implementation CVPR 2022 Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc van Gool

The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning.

Neural Network Compression

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models

no code implementations4 Apr 2022 Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab

One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced.

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

1 code implementation24 Mar 2022 Kai Zhang, Yawei Li, Jingyun Liang, JieZhang Cao, Yulun Zhang, Hao Tang, Radu Timofte, Luc van Gool

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.

Image Denoising Image-to-Image Translation

Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis

no code implementations5 Mar 2022 Yawei Li, Xin Wu, Ping Yang, Guoqian Jiang, Yuan Luo

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer.

BIG-bench Machine Learning Lung Cancer Diagnosis

VRT: A Video Restoration Transformer

1 code implementation28 Jan 2022 Jingyun Liang, JieZhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc van Gool

Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.

Deblurring Denoising +7

Video Super-Resolution Transformer

1 code implementation12 Jun 2021 JieZhang Cao, Yawei Li, Kai Zhang, Luc van Gool

Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.

Optical Flow Estimation Video Super-Resolution

LocalViT: Bringing Locality to Vision Transformers

2 code implementations12 Apr 2021 Yawei Li, Kai Zhang, JieZhang Cao, Radu Timofte, Luc van Gool

The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept.

Image Classification

Towards Efficient Graph Convolutional Networks for Point Cloud Handling

no code implementations ICCV 2021 Yawei Li, He Chen, Zhaopeng Cui, Radu Timofte, Marc Pollefeys, Gregory Chirikjian, Luc van Gool

In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds.

The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network

no code implementations1 Jan 2021 Yawei Li, He Chen, Zhaopeng Cui, Radu Timofte, Marc Pollefeys, Gregory Chirikjian, Luc van Gool

State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.

3D Classification Point Cloud Classification +1

Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation

no code implementations CVPR 2021 Rui Gong, Yuhua Chen, Danda Pani Paudel, Yawei Li, Ajad Chhatkuli, Wen Li, Dengxin Dai, Luc van Gool

Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.

Domain Adaptation Meta-Learning +2

Plug-and-Play Image Restoration with Deep Denoiser Prior

4 code implementations31 Aug 2020 Kai Zhang, Yawei Li, WangMeng Zuo, Lei Zhang, Luc van Gool, Radu Timofte

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.

Deblurring Demosaicking +1

The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures

1 code implementation CVPR 2021 Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte

Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.

Image Classification Image Restoration +1

Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training

1 code implementation CVPR 2021 Yunxuan Wei, Shuhang Gu, Yawei Li, Longcun Jin

The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i. e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner.

Image Super-Resolution Philosophy

DHP: Differentiable Meta Pruning via HyperNetworks

2 code implementations ECCV 2020 Yawei Li, Shuhang Gu, Kai Zhang, Luc van Gool, Radu Timofte

Passing the sparsified latent vectors through the hypernetworks, the corresponding slices of the generated weight parameters can be removed, achieving the effect of network pruning.

Denoising Image Classification +3

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

2 code implementations CVPR 2020 Yawei Li, Shuhang Gu, Christoph Mayer, Luc van Gool, Radu Timofte

In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.

Learning Filter Basis for Convolutional Neural Network Compression

3 code implementations ICCV 2019 Yawei Li, Shuhang Gu, Luc van Gool, Radu Timofte

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.

General Classification Image Classification +2

3D Appearance Super-Resolution with Deep Learning

1 code implementation CVPR 2019 Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc van Gool

Experimental results demonstrate that our proposed networks successfully incorporate the 3D geometric information and super-resolve the texture maps.


Word Recognition with Deep Conditional Random Fields

1 code implementation4 Dec 2016 Gang Chen, Yawei Li, Sargur N. Srihari

On the other hand, word recognition is a sequential problem where we need to model the correlation between characters.

Scene Recognition

Joint Visual Denoising and Classification using Deep Learning

1 code implementation4 Dec 2016 Gang Chen, Yawei Li, Sargur N. Srihari

Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model.

Classification Denoising +1

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