Sanja Fidler
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2017
Renewed Canada CIFAR AI Chair – 2023
Sanja Fidler is a Canada CIFAR AI Chair at the Vector Institute, an associate professor in the department of mathematical and computational sciences at the University of Toronto, and the director of AI at NVIDIA.
Fidler’s work is in the area of computer vision. Her main research interests are 2D and 3D object detection, particularly scalable multi-class detection, object segmentation and image labeling, and (3D) scene understanding. Fidler is also interested in the interplay between language and vision: generating sentential descriptions about complex scenes, as well as using textual descriptions for better scene parsing (e.g., in the scenario of the human-robot interaction).
Awards
- Best Paper Award, SIGGRAPH Asia, 2023
- Best Paper Honorable Mention, SIGGRAPH, 2023
- The Business Insider’s AI 100 2023: The top people in artificial intelligence
- Innovation Award, University of Toronto, 2021
- Connaught Innovation Award, 2020
- Early Researcher Award, 2019
- Connaught Innovation Award, 2018
- Best Paper Honorable Mention, CVPR, 2017
- Amazon Academic Research Award, 2017
- NVIDIA Pioneer of AI Award, 2016
- Facebook Faculty Award, 2016
Relevant Publications
- Wang, Z., Shen, T., Nimier-David, M., Sharp, N., Gao, J., Keller, A., Fidler, S., Müller, T., & Gojcic, Z. Adaptive Shells for Efficient Neural Radiance Field Rendering. SIGGRAPH Asia, 2023
- Lin, C-H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., … & Lin, T-Y. (2023). Magic3D: High-Resolution Text-to-3D Content Creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 300-309)
- Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S.W., Fidler, S., & Kreis K. Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models. In Computer Vision and Pattern Recognition (CVPR), 2023
- Gao, J., Shen, T., Wang, Z., Chen, W., Yin, K., Li D., … & Fidler S. (2022). Get3d: A generative model of high quality 3d textured shapes learned from images. Advances In Neural Information Processing Systems, 35:31841-31854.
- Shen, T., Gao, J., Yin, K., Liu, M-Y., & Fidler S. (2021). Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. In Advances in Neural Information Processing Systems, 34:6087-6101.
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019). Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3), 302-321.