About

I'm currently a Research Scientist at Meta Reality Labs.
Before that I was a visiting scholar in Carnegie Mellon University from Sept 2019 to Dec 2021, advised by Prof.Marios Savvides and Prof. Eric Xing.
I obtained my Ph.D. degree from Hong Kong University of Science and Technology in June 2021, supervised by Prof. Kwang-Ting Tim CHENG .
Before starting my Ph.D. study in HKUST in Sept. 2016, I obtained my bachelor’s degree from Fudan University in June. 2016.

Contact

Email: zliubq[at]connect[dot]ust[dot]hk; zechunl[at]andrew[dot]cmu[dot]edu
[Google Scholar] |  [Github]


Research Interest

My research interest lies at computer vision, machine learning, etc. Specifically, I am interested in using deep learning to solve the practical problems in the industry such as the limitation of insufficient resources and a trade-off between computation and accuracy. My research focus is mainly on:

  • Network binarization and quantization
  • Network channel pruning
  • Neural architecture design and search
  • Knowledge distillation
  • Image synthesizing
  • Few-shot learning

News

  • [May 2022] One paper accepted to ICML 2022.
  • [Mar 2022] Two papers accepted to CVPR 2022.
  • [Dec 2021] I joined Meta Inc. as a Research Scientise.
  • [Dec 2021] Two papers accepted to AAAI 2022.
  • [Sept 2021] One paper accepted to TIP 2021.
  • [May 2021] One paper accepted to ICML 2021.
  • [Apr 2021] I co-organized a workshop on CVPR 2021: Binary Networks for Computer Vision
  • [Mar 2021] One paper accepted to CVPR 2021.
  • [Jan 2021] One paper accepted to ICLR 2021.
  • [Dec 2020] One paper accepted to AAAI 2021 and one paper accepted to IJCV 2021.
  • [July 2020] Two papers accepted to ECCV 2020.
  • [March 2020] One paper accepted to CVPR 2020.
  • [Sept 2019] I come to Canegie Mellon University as a visiting scholar.

    • Publications

    Zechun Liu, Xiangyu Zhang, Zhiqiang Shen, Yichen Wei, Kwang-Ting Cheng, Jian Sun.
    Joint Multi-Dimension Pruning
    Accepted to IEEE Transactions on Image Processing (TIP) with minor revision.
    Paper

    Zechun Liu*, Zhiqiang Shen*, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng.
    How Do Adam and Training Strategies Help BNNs Optimization
    International Conference on Machine Learning (ICML), 2021.
    Code & Models  |  Paper

    Zechun Liu, Zhiqiang Shen, Marios Savvides, Kwang-Ting Cheng.
    ReActNet: Towards Precise Binary NeuralNetwork with Generalized Activation Functions
    European Conference on Computer Vision (ECCV), 2020.
    Code & Models  |  Paper
    We achieve 65.9% (ResNet-based) and 69.5% (MobileNet-based) top-1 accuracy on ImageNet (the new results are slightly higher than those in our original paper after we fix a small loading bug), for the first time, exceeding the benchmarking ResNet-level accuracy (69.3%) while achieving more than 22× reduction in computational complexity.

    Zhiqiang Shen, Zechun Liu, Jie Qin, Lei Huang, Kwang-Ting Cheng, Marios Savvides.
    S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
    Code & Models  |  arXiv Paper

    Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang.
    "BNN-BN=?": Training Binary Neural Networks without Batch Normalization
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, 2021. BNNs workshop as a spotlight.
    Code & Models  |  arXiv Paper

    Zhiqiang Shen, Zechun Liu, Dejia Xu, Zitian Chen, Kwang-Ting Cheng, Marios Savvides.
    Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
    International Conference on Learning Representations (ICLR), 2021.
    Project Page  |  Paper

    Zhiqiang Shen*, Zechun Liu*, Jie Qin, Marios Savvides, Kwang-Ting Cheng.
    Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
    Association for the Advancement of Artificial Intelligence (AAAI), 2021.
    Paper

    Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, Jian Sun.
    Single Path One-Shot Neural Architecture Search with Uniform Sampling
    European Conference on Computer Vision (ECCV), 2020.
    Code & Models  |  Paper

    Hai Phan*, Zechun Liu*, Dang Huynh, Marios Savvides, Kwang-Ting Cheng, Zhiqiang Shen.(*:Equal contribution)
    Binarizing MobileNet via Evolution-based Searching
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
    Paper

    Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Kwang-Ting Cheng, Jian Sun.
    MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
    IEEE International Conference on Computer Vision (ICCV), 2019.
    Code & Models  |  Paper

    Zechun Liu, Wenhan Luo, Baoyuan Wu, Xin Yang, Wei Liu, Kwang-Ting Cheng.
    Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance
    International Journal of Computer Vision (IJCV).
    Code & Models  |  Paper

    Zechun Liu, Baoyuan Wu, Wenhan Luo, Xin Yang, Wei Liu, Kwang-Ting Cheng.
    Bi-Real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm
    European Conference on Computer Vision (ECCV), 2018.
    Code & Models  |  Paper

    Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder.
    Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
    Advances in Neural Information Processing Systems 32 pre-proceedings (NIPS), 2019.
    Code & Models  |  Paper

    Academic Services

      Conference Reviewer
    • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, 2022
    • International Conference on Computer Vision (ICCV), 2021
    • European Conference on Computer Vision (ECCV), 2022
    • International Conference on Learning Representations (ICLR), 2022
    • Adcances in Neural Information Processing Systems (NeurIPS), 2021, 2022
    • International Conference on Machine Learning (ICML), 2022
      • Journal Reviewer
    • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 -- Present
    • IEEE Transactions on Image Processing (TIP), 2020 -- Present
    • IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020 -- Present

    Awards and Honors

    • SENG Academic Award for Continuing PhD Students 2019-20
    • Postgraduate Studentship, HKUST 2016-2020
    • Oversea Research Award, HKUST 2019
    • Shanghai Outstanding Graduate, Fudan University 2016
    • Chinese National Scholarship, Fudan University 2012-2013 & 2013-2014

    Teaching Assistant

    • 2017.2 - 2016.6, HKUST, ELEC 2300 Computer Organization
    • 2017.9 - 2018.1, HKUST, ELEC 4010K Machine Learning and Information Processing for Robotic Perception
    • 2017.2 - 2016.6, HKUST, ELEC 2300 Computer Organization