Leonid Sigal is a Canada CIFAR AI Chair at the Vector Institute, an associate professor in the department of computer science at the University of British Columbia, and a scientific advisor for Borealis AI.
Sigal’s research interests are primarily in computer vision, machine learning, and computer graphics. His research focuses on problems of visual and multi-modal (visual, textural, auditory) understanding and reasoning, including object recognition, scene understanding, articulated motion capture, action recognition, representation learning, manifold learning, transfer learning, character, and cloth animation.
- Recipient of NSERC Discovery Accelerator Supplement, 2018-2021
- Best Paper Award, IEEE Winter Conference of Applications of Computer Vision, 2018
Ma, S., Sigal, L., & Sclaroff, S. (2016). Learning activity progression in lstms for activity detection and early detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1942-1950).
Raptis, M., & Sigal, L. (2013). Poselet key-framing: A model for human activity recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2650-2657).
Sigal, L., Balan, A. O., & Black, M. J. (2010). Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International journal of computer vision, 87(1-2), 4.
Sigal, L., Bhatia, S., Roth, S., Black, M. J., & Isard, M. (2004). Tracking loose-limbed people. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 1, pp. I-I). IEEE.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.