Yihao LIU

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I am a Research Scientist at Shanghai Artificial Intelligence Laboratory, where I currently lead a team specializing in low-level vision research. I earned my Bachelor’s degree in 2018 and my Ph.D. in 2023, both from the University of Chinese Academy of Sciences (UCAS). During my doctoral studies, I was affiliated with the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, under the supervision of Prof. Yu Qiao and Prof. Chao Dong. My research focuses on computer vision, particularly image & video restoration and enhancement techniques, and their real-world applications.

Throughout my student journey, I have been honored with prestigious awards, including the President’s Award of the Chinese Academy of Sciences, the Zhu Li Yue Hua Outstanding Doctoral Student Award, the CAS Excellent Youth League Member Award, the Beijing Outstanding Graduate Award, and the SIAT President’s Innovation Award.

I have also excelled in multiple international and national competitions, such as 1st place in the PIRM 2018 Perceptual Image Super-Resolution Challenge, 1st place in the AIM 2020 Video Frame Interpolation Challenge, 2nd place in the NTIRE 2021 HDR Enhancement Challenge, 3rd place in the UDC 2020 Under-Display Camera Restoration Challenge. I serve as a reviewer for various top journals and conferences, including TPAMI, TIP, TCSVT, TMM, CVPR, ICCV, ECCV, NeurIPS, etc.

Current Research Focus:

  • Advanced Image and Video Restoration: Aiming to push the boundaries of restoration techniques to recover fine details and realistic textures from severely degraded inputs leveraging generative priors.
  • General Low-Level Vision Models: Developing unified multimodal frameworks that consolidate various low-level vision tasks (e.g., restoration, enhancement, style transfer, and feature extraction) into a single, multi-functional model.
  • Real-World Applications of Image Enhancement: Bridging the gap between laboratory research and real-world scenarios in low-level vision field. My research explores the application of low-level vision techniques in domains such as autonomous driving, smart imaging systems, and consumer devices.

I am open to collaboration and discussions. Feel free to reach out at liuyihao14@mails.ucas.ac.cn.

news

Jul 18, 2024 GenLV was accepted by ACM MM2024. GenLV is a successive work of PromptGIP, which further broadens the tasks and improves performance. The paper can be found at here.
Jul 01, 2024 Two papers were accepted by ECCV2024. By analyzing the relationships between image degradations, GRIDS propose a grouped learning method to deal with multiple-degradation restoration. X-Restormer is a new general image restoration backbone network, which possesses good task generality and achieves competitive performance across a variety of restoration tasks.
May 02, 2024 PromptGIP was accepted by ICML2024. PromptGIP is a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Code is available at here.
Aug 27, 2023 One paper was accepted by TPAMI. SRGA is the first quantitative indicator for measuring the generalization ability of blind super-resolution deep models.
Mar 25, 2023 Two papers were accepted by CVPR2023. DegAE is a new pretraining paradigm for low-level vision. MaskedDenoising adopts masked training to enhance the generalization performance of denoising networks.
Mar 12, 2023 Our video colorization method TCVC was accepted by CVMJ. Code is available at here.
Aug 25, 2022 Our survey on Blind Image Super-Resolution has been accepted by TPAMI. Paper Link
May 30, 2022 The extention version of the lightweight photo retouching network CSRNet has been accepted by TMM. Paper links: conference version, journal version.

selected publications

  1. ECCV
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    GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
    Shuo Cao*, Yihao Liu*, Wenlong Zhang, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision, 2024
  2. ECCV
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    A Comparative Study of Image Restoration Networks for General Backbone Network Design
    Xiangyu Chen, Zheyuan Li, Yuandong Pu, Yihao Liu, Jiantao Zhou, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision, 2024
  3. ICML
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    Unifying Image Processing as Visual Prompting Question Answering
    Yihao Liu*, Xiangyu Chen*, Xianzheng Ma*, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong
    In Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
  4. ACM MM
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    Learning A Low-Level Vision Generalist via Visual Task Prompt
    Xiangyu Chen, Yihao Liu, Yuandong Pu, Wenlong Zhang, Jiantao Zhou, Yu Qiao, and Chao Dong
    In Proceedings of the 32nd ACM International Conference on Multimedia, 2024
  5. CVMJ
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    Temporally Consistent Video Colorization with Deep Feature Propagation and Self-Regularization Learning
    Yihao Liu*, Hengyuan Zhao*, Kelvin CK Chan, Xintao Wang, Chen Change Loy, Yu Qiao, and Chao Dong
    Computational Visual Media, 2024
  6. CVPR
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    DegAE: A New Pretraining Paradigm for Low-Level Vision
    Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, and Chao Dong
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  7. TPAMI
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    Evaluating the Generalization Ability of Super-Resolution Networks
    Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, and Chao Dong
    IEEE Transactions on pattern analysis and machine intelligence, 2023
  8. CVPR
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    Masked Image Training for Generalizable Deep Image Denoising
    Haoyu Chen, Jinjin Gu, Yihao Liu, Salma Abdel Magid, Chao Dong, Qiong Wang, Hanspeter Pfister, and Lei Zhu
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  9. TPAMI
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    CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
    Mingye Xu, Yali Wang, Yihao Liu, Tong He, and Yu Qiao
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
  10. TMM
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    Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
    Yihao Liu*, Jingwen He*, Xiangyu Chen, Zhengwen Zhang, Hengyuan Zhao, Chao Dong, and Yu Qiao
    IEEE Transactions on Multimedia, 2022
  11. TPAMI
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    Blind Image Super-Resolution: A Survey and Beyond
    Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, and Chao Dong
    IEEE transactions on pattern analysis and machine intelligence, 2022
  12. TPAMI
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    RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
    Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
  13. TPAMI
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    Interactive Multi-Dimension Modulation for Image Restoration
    Jingwen He, Chao Dong, Yihao Liu, and Yu Qiao
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
  14. ICCV
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    Learn to Match: Automatic Matching Network Design for Visual Tracking
    Zhipeng Zhang, Yihao Liu, Xiao Wang, Bing Li, and Weiming Hu
    In International Conference on Computer Vision (ICCV), 2021
  15. arXiv
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    Discovering" Semantics" in Super-Resolution Networks
    Yihao Liu*, Anran Liu*, Jinjin Gu, Zhipeng Zhang, Wenhao Wu, Yu Qiao, and Chao Dong
    arXiv preprint arXiv:2108.00406, 2021
  16. AAAI
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    FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing
    Yu Dong*, Yihao Liu*, He Zhang, Shifeng Chen, and Yu Qiao
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020
  17. ECCV
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    Conditional Sequential Modulation for Efficient Global Image Retouching
    Jingwen He*, Yihao Liu*, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision (ECCV), 2020
  18. ECCVW
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    Enhanced Quadratic Video Interpolation
    Yihao Liu*, Liangbin Xie*, Li Siyao, Wenxiu Sun, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision (ECCV) Workshops, 2020
  19. ICCV
    RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
    Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao
    In International Conference on Computer Vision (ICCV), 2019
  20. ECCVW
    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy
    In Proceedings of the European conference on computer vision (ECCV) workshops, 2018