Research

In high school, I was obsessed with geometry and did a full survey on generalised Fermat problem. I then further studied its related NP-Hard Euclidean Steiner Tree problem during my undergrad study.

Currently, I am interested in machine learning, in particular meta-learning, self-supervised learning and multi-task learning with applications in vision and robotics. The following is my publications displayed in chronological order.

Thesis

Publication

  • Shape Adaptor: A Learnable Resizing Module
    European Conference on Computer Vision (ECCV), 2020
    Shikun Liu, Zhe Lin, Yilin Wang, Jianming Zhang, Federico Perazzi, and Edward Johns
    [paper] [arxiv] [code] [slide]
  • Self-Supervised Generalisation with Meta Auxiliary Learning
    Advances in Neural Information Processing Systems (NeurIPS), 2019
    Shikun Liu, Andrew J. Davison and Edward Johns
    [paper] [arxiv] [code] [poster] [slide]
  • End-to-End Multi-task Learning with Attention
    Computer Vision and Pattern Recognition (CVPR), 2019
    Shikun Liu, Edward Johns, and Andrew J. Davison
    [paper] [arxiv] [code]
  • Learning a Hierarchical Latent-Variable Model of 3D Shapes
    International Conference on 3D Vision (3DV), 2018 [ORAL]
    Shikun Liu, C. Lee Giles, and Alexander G. Ororbia II.
    [paper] [supplementary] [arxiv] [code] [project page]