[26] Jiangtao Zhang, Zongsheng Yue, Hui Wang, Qian Zhao and Deyu Meng. Blind image deconvolution by generative-based kernel prior and initializer via latent encoding. In: Proceedings of the 18th European Conference on Computer Vision (ECCV), Milan, Italy, 2024.
[25] Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng and Yefeng Zheng. Interactive segmentation as Gaussian process classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023.
[24] Jiahong Fu, Hong Wang, Qi Xie, Qian Zhao, Deyu Meng and Zongben Xu. KXNet: A model-driven deep neural network for blind super-resolution. In: Proceedings of the 17th European Conference on Computer Vision (ECCV), Tel Aviv, Israel, 2022.
[23] Zongsheng Yue, Qian Zhao, Jianwen Xie, Lei Zhang, Deyu Meng and Kwan-Yee K Wong. Blind image super-resolution with elaborate degradation modeling on noise and kernel. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022.
[22] Zongsheng Yue, Jianwen Xie, Qian Zhao and Deyu Meng. Semi-supervised video deraining with dynamical rain generator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online, 2021.
[21] Hong Wang, Zongsheng Yue, Qi Xie, Qian Zhao, Yefeng Zheng and Deyu Meng. From rain generation to rain removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online, 2021.
[20] Xiangyu Rui, Xiangyong Cao, Qi Xie, Zongsheng Yue, Qian Zhao and Deyu Meng. Learning an explicit weighting scheme for adapting complex HSI noise. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online, 2021.
[19] Yichen Wu, Jun Shu, Qi Xie, Qian Zhao and Deyu Meng. Learning to purify noisy labels via meta soft label corrector. In: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Online, 2021.
[18] Zongsheng Yue, Qian Zhao, Lei Zhang and Deyu Meng. Dual adversarial network: Toward real noise removal and noise generation. In: Proceedings of the 16th European Conference on Computer Vision (ECCV), Online, 2020.
[17] Hong Wang, Qi Xie, Qian Zhao and Deyu Meng. A model-driven deep neural network for single image rain removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online, 2020.
[16] Zhengyang Hu, Jiang Xue, Deyu Meng, Qian Zhao and Zongben Xu. MEP-Based channel estimation under complex communication environment. In: Proceedings of the IEEE International Conference on Communications (ICC), Online, 2020.
[15] Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu and Deyu Meng. Meta-weight-net: learning an explicit mapping for sample weighting. In: Advances in Neural Information Processing Systems 32 (NeurIPS), Vancouver, Canada, 2019.
[14] Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang and Deyu Meng. Variational denoising network: toward blind noise modeling and removal. In: Advances in Neural Information Processing Systems 32 (NeurIPS), Vancouver, Canada, 2019.
[13] Qi Xie, Minghao Zhou, Qian Zhao, Deyu Meng, Wangmeng Zuo and Zongben Xu. Multispectral and hyperspectral image fusion by MS/HS fusion net. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019.
[12] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu and Ying Wu. Semi-supervised transfer learning for image rain removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019.
[11] Haipei Zhang, Jiang Xue, Deyu Meng, Qian Zhao and Zongben Xu. Robust CSI estimation under complex communication environment. In: Proceedings of the IEEE International Conference on Communications (ICC), Shanghai, China, 2019.
[10] Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao and Deyu Meng. Video rain streak removal by multiscale convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018.
[9] Wei Wei, Lixuan Yi, Qi Xie, Qian Zhao, Deyu Meng and Zongben Xu. Should we encode rain streaks in video as deterministic or stochastic? In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017.
[8] Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu, Shuhang Gu, Wangmeng Zuo and Lei Zhang. Multispectral images denoising by intrinsic tensor sparsity regularization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.
[7] Xi’ai Chen, Zhi Han, Yao Wang, Qian Zhao, Deyu Meng and Yandong Tang. Robust tensor factorization with unknown noise. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.
[6] Qian Zhao, Deyu Meng, Xu Kong, Qi Xie, Wenfei Cao, Yao Wang and Zongben Xu. A novel sparsity measure for tensor recovery. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.
[5] Xiangyong Cao, Yang Chen, Qian Zhao, Deyu Meng, Yao Wang, Dong Wang and Zongben Xu. Low-rank matrix factorization under general mixture noise distributions. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.
[4] Dingwen Zhang, Deyu Meng, Chao Li, Lu Jiang, Qian Zhao and Junwei Han. A self-paced multiple-instance learning framework for co-saliency detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.
[3] Qian Zhao, Deyu Meng, Lu Jiang, Qi Xie, Zongben Xu and Alexander Hauptmann. Self-paced learning for matrix factorization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), Austin, USA, 2015.
[2] Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan and Alexander Hauptmann. Self-paced curriculum learning. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), Austin, USA, 2015.
[1] Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo and Lei Zhang. Robust principal component analysis with complex noise. In: Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014.