[Main] [Publication] [Google Scholar] [Code and Data]

2023

Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
Hao Hou, Jun Xu*, Yingkun Hou, Xiaotao Hu, Benzheng Wei, Dinggang Shen
IEEE Transactions on Image Processing (TIP), accept, Jan. 2023.
[Paper] [Supp] [Arxiv] [Github]


2022

Application of Deep Learning for Digital Subtraction Angiography Imaging: A Potential New Method for Radiation Dose Reduction
Zhijiang Tang#, Xuantai Wu#, Ruijue Wang#, Tianyi Xu, Qiyu Zhao, Yuxuan Shi, Ximing Xu, Jun Xu*, Dinggang Shen*, Qiang Xiong*
Submitted, July 2022.
[Paper] [Supp] [Arxiv] [Github]


Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark
Gang Xu#, Yuchen Yang#, Jun Xu*, Liang Wang, Xian-Tong Zhen, Ming-Ming Cheng
Submitted, July 2022.
[Paper] [Supp] [Arxiv] [Github]


Delving Deeper into the Uncertainty in Gaze Estimation
Guan-He Huang#, Jing-Yue Shi#, Jun Xu#, Jing Li, Ying-Jun Du, Xian-Tong Zhen
Submitted, May 2022.
[Paper] [Supp] [Arxiv] [Github]


Fusion-Correction Network for Single-Exposure Correction and Multi-Exposure Fusion
Jin Liang#, Yuchen Yang#, Anran Zhang, Jun Xu*, Hui Li, Xiantong Zhen
Submitted, Mar. 2022.
[Paper] [Supp] [Arxiv] [Github]


Restore Globally, Refine Locally: A Mask-Guided Scheme to Accelerate Super-Resolution Networks
Xiaotao Hu, Jun Xu*, Shuhang Gu, Ming-Ming Cheng, Li Liu
European Conference on Computer Vision (ECCV), 2022. (Oral Presentation)
[Paper] [Supp] [Github]


Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis
Chaowei Fang#, Liang Wang#, Dingwen Zhang, Jun Xu, Yixuan Yuan, Junwei Han
Computer Vision and Pattern Recognition (CVPR), 2022.
[Paper] [Arxiv] [Github]


NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte, Liangyu Chen, Xiaojie Chu, Wenqing Yu, Kai Jin, Zeqiang Wei, Sha Guo, Angulia Yang, Xiuzhuang Zhou, Guodong Guo, Bin Dai, Feiyue Peng, Huaxin Xiao, Shen Yan, Yuxiang Liu, Hanxiao Cai, Pu Cao, Yang Nie, Lu Yang, Qing Song, Xiaotao Hu, Jun Xu, et al.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 906-919, 2022. (We ranked 5th in this challenge.)
[Paper] [Supp] [Arxiv] [Github]


MetaKernel: Learning Variational Random Features with Limited Labels
Yingjun Du, Haoliang Sun, Xiantong Zhen, Jun Xu, Yilong Yin, Ling Shao, Cees GM Snoek
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), accept.
[Paper] [Arxiv] [Github]


Cross-Domain Attention Network for Unsupervised Domain Adaptation Crowd Counting
Anran Zhang#, Jun Xu#, Xiaoyan Luo, Xianbin Cao, Xiantong Zhen
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), accept.
[Paper] [Github]


Latent Domain Generation for Unsupervised Domain Adaptation Object Counting
Anran Zhang, Yandan Yang, Jun Xu, Xianbin Cao, Xiantong Zhen, Ling Shao
IEEE Transactions on Multimedia (TMM), accept.
[Paper] [Github]

2021

Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
Gang Xu, Jun Xu*, Zhen Li, Liang Wang, Xing Sun, Ming-Ming Cheng
Computer Vision and Pattern Recognition (CVPR), 2021.
[Paper] [Supp] [Arxiv] [Github]


MobileSal: Extremely Efficient RGB-D Salient Object Detection
Yu-Huan Wu, Yun Liu, Jun Xu, Jiawang Bian, Yuchao Gu, Ming-Ming Cheng
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), accept.
[Paper] [Arxiv] [Github]


Deep Hough Transform for Semantic Line Detection
Kai Zhao#, Qi Han#, Changbin Zhang, Jun Xu, Ming-Ming Cheng
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), accept.
[Paper] [Arxiv] [Github] [Demo]


CDNet: Complementary Depth Network for RGB-D Salient Object Detection
Wenda Jin#, Jun Xu#, Qi Han, Yi Zhang, Ming-Ming Cheng
IEEE Transactions on Image Processing (TIP), vol. 30, 3376-3390, Mar. 2021.
[Paper] [Github]


JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
Yuhuan Wu, Shanghua Gao, Jie Mei, Jun Xu, Dengping Fan, Rongguo Zhang, Ming-Ming Cheng
IEEE Transactions on Image Processing (TIP), vol. 30, pp. 3113-3126, Feb. 2021.
[Paper] [Arxiv] [Github]


Bilateral Attention Network for RGB-D Salient Object Detection
Zhao Zhang, Zheng Lin, Jun Xu, Wenda Jin, Shaoping Lu, Dengping Fan
IEEE Transactions on Image Processing (TIP), vol. 30, pp. 1949-1961, Jan. 2021.
[Paper] [Arxiv] [Github] [Bibtex]


Scaled Simplex Representation for Subspace Clustering
Jun Xu*, Mengyang Yu, Ling Shao, Wangmeng Zuo, Deyu Meng, Lei Zhang, David Zhang
IEEE Transactions on Cybernetics (TCYB), vol. 51, issue 3, pp. 1493-1505, Mar. 2021.
[Paper] [Supp] [Arxiv] [Github] [Bibtex]


Pixel-level Non-local Image Smoothing with Objective Evaluation
Jun Xu#, Zhi-Ang Liu#, Ying-Kun Hou, Xiantong Zhen, Ling Shao, Ming-Ming Cheng
IEEE Transactions on Multimedia (TMM), 23, pp. 4065-4078, Nov. 2021.
[Paper] [Pdf] [Supplementary File] [Github]

2020

ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Wen-Da Jin#, Jun Xu#, Ming-Ming Cheng, Yi Zhang, Wei Guo
Neural Information Processing Systems (NeurIPS), Sep. 2020.
[Paper] [Github]


Learning to Learn Kernels with Variational Random Features
Xiantong Zhen#, Haoliang Sun#, Yingjun Du#, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
International Conference on Machine Learning (ICML), July 2020.
[Paper] [Arxiv] [Github]


Learning to Learn with Variational Information Bottleneck for Domain Generalization
Yingjun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees Snoek, Ling Shao
European Conference on Computer Vision (ECCV), Aug. 2020.
[Paper] [Github]


Deep Hough Transform for Semantic Line Detection
Qi Han#, Kai Zhao#, Jun Xu, Ming-Ming Cheng
European Conference on Computer Vision (ECCV), Aug. 2020.
[Arxiv] [Github]


Gradient-Induced Co-Saliency Detection
Zhao Zhang#, Wen-Da Jin#, Jun Xu, Ming-Ming Cheng
European Conference on Computer Vision (ECCV), Aug. 2020.
[Arxiv] [Chinese Version] [Short Video (1m)] [Long Video (6m'30s)] [Slides] [Github] [CoCA Dataset] [Bibtex]


STAR: A Structure and Texture Aware Retinex Model
Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, and Ling Shao
IEEE Transactions on Image Processing (TIP), vol. 29, pp. 5022-5037, Mar. 2020.
[Paper] [Arxiv] [Github] [Bibtex]


Noisy-As-Clean: Learning Self-supervised Denoising from Corrupted Image
Jun Xu#, Yuan Huang#, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, Ling Shao
IEEE Transactions on Image Processing (TIP), vol. 29, pp. 9316-9329, Sep. 2020.
[Paper] [Arxiv] [Github]


Conditional Variational Image Deraining
Yingjun Du#, Jun Xu#, Xiantong Zhen, Ming-Ming Cheng, Ling Shao
IEEE Transactions on Image Processing (TIP), vol. 29, issue 1, pp. 6288-6301, May 2020.
[Paper] [Arxiv] [Github] [Bibtex]


NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
Yingkun Hou, Jun Xu*, Mingxia Liu, Guanghai Liu, Li Liu, Fan Zhu, Ling Shao
IEEE Transactions on Image Processing (TIP), vol. 29, pp. 5121-5135, Mar. 2020.
[Paper] [Arxiv] [Github] [Bibtex]


PID Controller based Stochastic Optimization Acceleration for Deep Neural Networks
Haoqian Wang, Yi Luo, Wangpeng An, Qingyun Sun, Jun Xu, Lei Zhang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 31, issue 12, pp. 5079-5091, Dec. 2020. (Extension of our CVPR 2018 work)
[Paper] [Github] [Bibtex]

2019

RANet: Ranking Attention Network for Fast Video Object Segmentation
Ziqin Wang, Jun Xu*, Li Liu, Fan Zhu, and Ling Shao
International Conference on Computer Vision (ICCV), Seoul, Korea, 2019.
[Paper] [Supp] [Arxiv] [Github] [DAVIS-2016 Benchmark] [DAVIS-2017 Benchmark] [Bibtex]


Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
Jun Xu*, Wangpeng An, David Zhang, Lei Zhang
Pattern Recognition, vol. 88, pp. 679-688, Apr. 2019.
[Paper] [Arxiv] [Github] [Bibtex]

2018 and Before

Nonlocal Self-Similarity Based Prior Modeling for Image Denoising
Jun Xu
Ph.D. Thesis, May 2018.
[Thesis] [Slides] (Broken Link) [Bibtex]


Real-world Noisy Image Denoising: A New Benchmark
Jun Xu, Hui Li, Zhetong Liang, David Zhang, Lei Zhang
arxiv:1804.02603
[Arxiv] [Dataset]


A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising
Jun Xu, Lei Zhang, David Zhang
European Conference on Computer Vision (ECCV), Munich, Germany, 2018.
[Paper] [Supp] [Poster] [Github] [Bibtex]


A Hybrid L1-L0 Layer Decomposition Model for Tone Mapping
Zhetong Liang, Jun Xu, David Zhang, Zisheng Cao, Lei Zhang
Computer Vision and Pattern Recognition (CVPR), pp. 4758-4766, Salt Lake City, USA, 2018.
[Paper] [Supp] [Code] [Bibtex]


A PID Controller Approach for Stochastic Optimization of Deep Networks
Wangpeng An, Haoqian Wang, Qingyun Sun, Jun Xu, Qionghai Dai, Lei Zhang
Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018.
[Paper] [Supp] [Code] [Bibtex]


External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising
Jun Xu, Lei Zhang, David Zhang
IEEE Transactions on Image Processing (TIP), vol. 27, issue 6, pp. 2996-3010, June 2018.
[Paper] [Supp] [Github] [Bibtex]


Partial Deconvolution with Inaccurate Blur Kernel
Dongwei Ren, Wangmeng Zuo, David Zhang, Jun Xu, Lei Zhang
IEEE Transactions on Image Processing (TIP), vol. 27, issue 1, pp. 511-524, Jan. 2018.
[Paper] [Bibtex]


Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising
Jun Xu, Lei Zhang, David Zhang, Xiangchu Feng
International Conference on Computer Vision (ICCV), 1096-1104, Venice, Italy, 2017.
[Paper] [Supp] [Poster] [Github] [Bibtex]


Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
Jun Xu, Lei Zhang, Wangmeng Zuo, David Zhang and Xiangchu Feng
International Conference on Computer Vision (ICCV), 244-252, Santiago, Chile, 2015.
[Paper] [Supp] [Poster] [Code] [Bibtex]


Reweighted Sparse Subspace Clustering
Jun Xu, Kui Xu, Ke Chen, Jishou Ruan
Computer Vision and Image Understanding (CVIU), vol. 138, pp. 25-37, Sep. 2015.
[Paper] [Code] [Freiburg-Berkeley Motion Segmentation Dataset (137 Sequences, 54.7M)] [Bibtex]

All things are difficult before they are easy.
— Thomas Fuller