Kristen Grauman
Professor
Research
- Computer vision
- Machine learning
Within computer vision and machine learning, Prof. Grauman's primary interests are visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning.
Computer Vision Group Electrical and Computer Engineering Department GSC
Select Publications
2.5D Visual Sound. R. Gao and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019.
Discovering Important People and Objects for Egocentric Video Summarization. Y. J. Lee, J. Ghosh, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012.
Kernelized Locality-Sensitive Hashing. B. Kulis and K. Grauman. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 34, No. 6, June 2012.
Relative Attributes. D. Parikh and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. (Marr Prize, ICCV Best Paper Award)
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. K. Grauman and T. Darrell. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005. (Helmholtz Prize, test of time award)
Awards & Honors
- 2019 - AAAI Fellow
- 2018 - J. K. Aggarwal Prize, International Association for Pattern Recognition
- 2017 - Helmholtz Prize
- 2017 - UT Austin Academy of Distinguished Teachers
- 2016 - Best Paper Award, Asian Conference on Computer Vision
- 2014 - Presidential Early Career Award for Scientists and Engineers
- 2013 - Computers and Thought Award, International Joint Conferences on Artificial Intelligence
- 2013 - Pattern Analysis and Machine Intelligence Young Researcher Award
- 2012 - Alfred P. Sloan Research Fellow
- 2011 - Marr Prize