Research Scientist @ Samsung AI Centre, Cambridge

Visiting Researcher @ University of Edinburgh

E-mail: dali.academic(at)gmail.com
Google Scholar

Da Li is a Research Scientist within the Machine Learning and Data Intelligence group in Samsung AI Centre Cambridge, and also a Visiting Researcher of the Machine Intelligence Research group at the University of Edinburgh.

Da served/serves as reviewers for BMVC, CVPR, NeurIPS, ICML, TPAMI, ML.

Da's research interests span transfer learning, meta learning and semi-supervised learning.

  • [05/2021] One paper about adversarial robustness is accepted in ICML 2021. Congrats Panagiotis!
  • [12/2020] One paper about stochastic neural networks is accepted in AAAI 2021.
  • [10/2020] One paper about sketch based photo segmenter generation is accepted in TIP.
  • [07/2020] Our 'online Meta-DA for MSDA/SSDA' accepted in ECCV 2020.
  • [12/2019] Code released for our ICCV'19 paper "Episodic DG".
  • [11/2019] Code released for our ICCV'19 paper "Robust Person Re-identification".
  • [07/2019] Two papers accepted in ICCV 2019, including one oral.
  • [07/2019] Joined Machine Learning Group of SAIC-C as a Researcher working with Dr. Timothy Hospedales.
  • [01/2019] Joined SAIC-Cambridge as a Research Intern working with Prof. Tao Xiang.
  • [07/2018] Our paper accepted in ECCV 2018.
  • [06/2018] Code released for our AAAI 2018 paper "Meta DG".
  • [03/2018] Our paper has been selected for the spotlight presentation in CVPR 2018.
  • [03/2018] Awarded for the Rabin Ezra Scholarship.
  • [02/2018] One recent paper will appear in CVPR 2018.
  • [11/2017] One paper will appear in AAAI 2018.
  • Visiting Researcher, University of Edinburgh, UK. 2020/July-Present.
  • Research Scientist, Samsung AI Center, Cambridge, UK. 2019/July-Present.
  • Research Intern, Samsung AI Center, Cambridge, UK. 2019/Jan-July.
  • Research Assistant, SketchX Lab (QMUL/CVSSP-Surrey), UK. 2016/Sept-2019/July.
  • Computer Vision Engineer, Westwell-Lab, Shanghai, China. 2016/Feb-Aug.
  • Software Engineer, Autodesk, Shanghai, China. 2014/July-2016/Feb.

2021

Weight-Covariance Alignment for Adversarially Robust Neural Networks
Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
Accepted in ICML 2021



Simple and Effective Stochastic Neural Networks
Tianyuan Yu, Yongxin Yang, Da Li, Timothy Hospedales and Tao Xiang
Accepted in AAAI 2021



2020

Sketch-a-Segmenter: Sketch-based Photo Segmenter Generation
Conghui Hu, Da Li, Yongxin Yang, Timothy Hospedales and Yi-Zhe Song
Accepted in Transactions on Image Processing



Sequential Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song and Timothy Hospedales
Accepted in TASK-CV Workshop at ECCV 2020



Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
Da Li, Timothy Hospedales
Accepted in ECCV 2020



2019

Robust Person Re-identification by Modelling Feature Uncertainty
Tianyuan Yu, Da Li, Yongxin Yang, Timothy Hospedales, Tao Xiang
Accepted in ICCV 2019

Code


Episodic training for domain generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, Timothy M Hospedales
Accepted as an oral in ICCV 2019

Code


2018

Deep factorised inverse-sketching
Kaiyue Pang, Da Li, Jifei Song, Yi-Zhe Song, Tao Xiang, Timothy M Hospedales
Accepted in ECCV 2018



Sketch-a-Classifier: Sketch-based Photo Classifier Generation
Conghui Hu, Da Li, Yi-Zhe Song, Tao Xiang, Tim Hospedales
Accepted as a spotlight in Conference on Computer Vision and Pattern Recognition (CVPR 2018)



Learning to Generalize: Meta-Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song and Timothy M. Hospedales
Accepted in AAAI Conference on Artificial Intelligence (AAAI 2018)

Code


2017

Deeper, Broader and Artier Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song and Timothy M. Hospedales
International Conference on Computer Vision (ICCV), 2017.

Project

Now You See Me: Deep Face Hallucination for Unviewed Sketches
Conghui Hu, Da Li, Yi-Zhe Song and Timothy M. Hospedales
British Machine Vision Conference (BMVC) -- oral, 2017.