Alumni Profiles

Kristen Grauman

• Program Years: 2001-2005
• Academic Institution: Massachusetts Institute of Technology
• Field of Study: Computer Science
• Academic Advisor: Trevor Darrell
• Practicum(s):
  Lawrence Berkeley National Laboratory (2003)
• Degree(s):
  Ph.D. Computer Science, Massachusetts Institute of Technology, 2006
  S.M. Computer Science, Massachusetts Institute of Technology, 2003
  B.A. Computer Science, Boston College, 2001
  

Current Status

• Status: Associate Professor, Dept. of Computer Science, University of Texas at Austin
• Research Area: Computer Vision, Machine Learning
• Personal URL: http://www.cs.utexas.edu/~grauman/

Publications

Papers available at: http://www.cs.utexas.edu/~grauman/

Book
1. K. Grauman and B. Leibe. Visual Object Recognition. Synthesis Lectures on Artificial Intelligence
and Machine Learning. Morgan and Claypool Publishers, April 2011, Vol. 5, No. 2,
Pages 1-181.
Book chapters
1. K. Grauman and R. Fergus. Learning Binary Hash Codes for Large-Scale Image Search.
Invited chapter, in Machine Learning for Computer Vision, Studies in Computational Intelligence
Series. R. Cipolla, S. Battiato, and G. Farinella, Editors. Springer. Vol. 411, pp. 49-87,
2013.
2. S. Vijayanarasimhan and K. Grauman. Minimizing Annotation Costs in Visual Category
Learning. Invited chapter, in Cost-Sensitive Machine Learning, B. Krishnapuram, S. Yu, and
B. Rao, Editors. Chapman and Hall/CRC, December 2011.
3. K. Grauman and T. Darrell. Contour Matching Using Approximate Earth Mover’s Distance,
chapter in Nearest Neighbors in Learning and Vision: Theory and Practice, T. Darrell, P.
Indyk, G. Shakhnarovich, Editors. MIT Press, 2005.
Journal articles
1. Y. J. Lee and K. Grauman. Predicting Important Objects for Egocentric Video Summarization.
International Journal of Computer Vision (IJCV), Volume 114, Issue 1, pp. 38-55. August
2015.
2. J. Kim and K. Grauman. Boundary Preserving Dense Local Regions. IEEE Trans. on Pattern
Analysis and Machine Intelligence (PAMI), Volume 37, No. 5, pp. 931-943, May 2015.
3. A. Kovashka and K. Grauman. Discovering Attribute Shades of Meaning with the Crowd.
International Journal of Computer Vision (IJCV), Volume 114, Issue 1, pp 56-73. August
2015.
4. A. Kovashka, D. Parikh, and K. Grauman. WhittleSearch: Interactive Image Search with
Relative Attribute Feedback. International Journal of Computer Vision (IJCV), Volume 115,
Issue 2, pp 185-210, November 2015.
5. S. Vijayanarasimhan and K. Grauman. Large-Scale Live Active Learning: Training Object
Detectors with Crawled Data and Crowds. International Journal of Computer Vision (IJCV),
Volume 108, Issue 1-2, pp. 97-114, May 2014.
6. B. Gong, K. Grauman, and F. Sha. Learning Kernels for Unsupervised Domain Adaptation
with Applications to Visual Object Recognition. International Journal of Computer Vision
(IJCV), Volume 109, Issue 1-2, pp. 3-27, August 2014.
7. S. Vijayanarasimhan, P. Jain, and K. Grauman. Hashing Hyperplane Queries to Near Points
with Applications to Large-Scale Active Learning. Transactions on Pattern Analysis and
Machine Intelligence (PAMI), Vol. 36, No. 2, pp. 276-288, February 2014.
8. Y. J. Lee and K. Grauman. Object-Graphs for Context-Aware Visual Category Discovery.
IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI). Vol. 34, No. 2, pp.
346-358, February 2012.
9. B. Kulis and K. Grauman. Kernelized Locality-Sensitive Hashing. IEEE Trans. on Pattern
Analysis and Machine Intelligence (PAMI). Vol. 34, No. 6, pp. 1092-1104, June 2012.
10. S. J. Hwang and K. Grauman. Reading Between the Lines: Object Localization Using Implicit
Cues from Image Tags. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI).
Vol. 34, No. 6, pp. 1145-1158, June 2012.
11. S. J. Hwang and K. Grauman. Learning the Relative Importance of Objects from Tagged
Images for Retrieval and Cross-Modal Search. International Journal of Computer Vision
(IJCV). Vol. 100, Issue 2, pp. 134-153, November 2012. [Invited article]
12. S. Vijayanarasimhan and K. Grauman. Cost-Sensitive Active Visual Category Learning. International
Journal of Computer Vision (IJCV), Vol. 91, No. 1, pp. 24–44, July 2010.
13. K. Grauman. Efficiently Searching for Similar Images. Communications of the ACM (CACM),
Vol. 53 No. 6, pp. 84–94, June 2010. [Invited article]
14. B. Kulis, P. Jain, and K. Grauman. Fast Similarity Search for Learned Metrics. IEEE
Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, No. 12, pp. 2143–2157,
Dec 2009. [Invited article for best papers of CVPR 2008]
15. Y. J. Lee and K. Grauman. Foreground Focus: Unsupervised Learning from Partially Matching
Images. International Journal of Computer Vision (IJCV), Vol. 85, No. 2, pp. 143–166, May
2009.
16. M. S. Ryoo, K. Grauman, and J. K. Aggarwal. A Task-Driven Intelligent Workspace System
to Provide Guidance Feedback. Computer Vision and Image Understanding (CVIU), Vol. 114, No. 5, pp. 520–534, May 2010.
17. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Gaussian Processes for Object Categorization.
International Journal of Computer Vision (IJCV), Vol. 88, No. 2, pp. 169–188,
July 2009.
18. K. Grauman and T. Darrell. The Pyramid Match Kernel: Efficient Learning with Sets of
Features. Journal of Machine Learning Research (JMLR), No. 8, pp. 725–760, April 2007.
19. K. Grauman, M. Betke, J. Lombardi, J. Gips, and G. Bradski. Communication via Eye
Blinks and Eyebrow Raises: Video-Based Human-Computer Interfaces. Universal Access in
the Information Society, Springer-Verlag Heidelberg, Vol. 2, No. 4, pp. 359–373, November
2003.
Peer-reviewed conference papers (acceptance rates typically ∼ 3%-25%)
1. D. Jayaraman and K. Grauman. Learning Image Representations Tied to Ego-Motion. In
Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago,
Chile, December 2015. (oral presentation, ∼4% acceptance rate)
2. A. Yu and K. Grauman. Just Noticeable Differences in Visual Attributes. In Proceedings of
the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, December
2015.
3. W-L. Chao, B. Gong, K. Grauman, and F. Sha. Large-Margin Determinantal Point Processes.
In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam,
Netherlands, July 2015.
4. A. Yu and K. Grauman. Predicting Useful Neighborhoods for Lazy Local Learning. In Advances
in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.
5. D. Jayaraman and K. Grauman. Zero-shot Recognition with Unreliable Attributes. In Advances
in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.
6. B. Gong, W. Chao, K. Grauman, and F. Sha. Diverse Sequential Subset Selection for Supervised
Video Summarization. In Advances in Neural Information Processing Systems (NIPS),
Montreal, Canada, Dec 2014.
7. C.-Y. Chen and K. Grauman. Predicting the Location of “Interactees” in Novel Human-Object
Interactions. In Proceedings of the Asian Conference on Computer Vision (ACCV), Singapore,
Nov 2014.
8. S. Jain and K. Grauman. Which Image Pairs Will Cosegment Well? Predicting Partners
for Cosegmentation. In Proceedings of the Asian Conference on Computer Vision (ACCV),
Singapore, Nov 2014.
9. B. Xiong and K. Grauman. Detecting Snap Points in Egocentric Video with a Web Photo Prior.
In Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland,
Sept 2014.
10. S. Jain and K. Grauman. Supervoxel-Consistent Foreground Propagation in Video. In Proceedings
of the European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept
2014.
11. D. Jayaraman, F. Sha, and K. Grauman. Decorrelating Semantic Visual Attributes by Resisting
the Urge to Share. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Columbus, Ohio, June 2014. (oral presentation, 5.75% acceptance
rate)
12. A. Yu and K. Grauman. Fine-Grained Visual Comparisons with Local Learning. In Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus,
Ohio, June 2014.
13. L. Liang and K. Grauman. Beyond Comparing Image Pairs: Setwise Active Learning for
Relative Attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Columbus, Ohio, June 2014.
14. C.-Y. Chen and K. Grauman. Inferring Unseen Views of People. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June
2014.
15. C.-Y. Chen and K. Grauman. Inferring Analogous Attributes. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June
2014.
16. A. Kovashka and K. Grauman. Attribute Pivots for Guiding Relevance Feedback in Image
Search. In Proceedings of the IEEE International Conference on Computer Vision (ICCV),
Sydney, Australia, December 2013.
17. S. Jain and K. Grauman. Predicting Sufficient Annotation Strength for Interactive Foreground
Segmentation. In Proceedings of the IEEE International Conference on Computer Vision
(ICCV), Sydney, Australia, December 2013.
18. A. Kovashka and K. Grauman. Attribute Adaptation for Personalized Image Search. In
Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia,
December 2013.
19. S. Bandla and K. Grauman. Active Learning of an Action Detector from Untrimmed Videos.
In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney,
Australia, December 2013.
20. B. Gong, K. Grauman, and F. Sha. Reshaping Visual Datasets for Domain Adaptation. In
Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, December
2013.
21. D. Parikh and K. Grauman. Implied Feedback: Learning Nuances of User Behavior in Image
Search. In Proceedings of the IEEE International Conference on Computer Vision (ICCV),
Sydney, Australia, December 2013.
22. C.-Y. Chen and K. Grauman. Watching Unlabeled Video Helps Learn New Human Actions
from Very Few Labeled Snapshots. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. (oral presentation,
3.2% acceptance rate)
23. Z. Lu and K. Grauman. Story-Driven Summarization for Egocentric Video. In Proceedings of
the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR,
June 2013.
24. J. Kim, C. Liu, F. Sha, and K. Grauman. Deformable Spatial Pyramid Matching for Fast
Dense Correspondences. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Portland, OR, June 2013.
25. S. J. Hwang, K. Grauman, and F. Sha. Analogy-Preserving Semantic Embedding for Visual
Object Categorization. In Proceedings of the International Conference on Machine Learning
(ICML), Atlanta, GA, June 2013.
26. B. Gong, K. Grauman, and F. Sha. Connecting the Dots with Landmarks: Discriminatively
Learning Domain-Invariant Features for Unsupervised Domain Adaptation. In Proceedings of
the International Conference on Machine Learning (ICML), Atlanta, GA, June 2013. (full
oral presentation)
27. T. McCandless and K. Grauman. Object-Centric Spatio-Temporal Pyramids for Egocentric
Activity Recognition. In Proceedings of the British Machine Vision Conference (BMVC),
Bristol, UK, Sept 2013.
28. A. Luong, M. Gerbush, B. Waters, and K. Grauman. Reconstructing a Fragmented Face from
an Attacked Secure Identification Protocol. In IEEE Workshop on Applications of Computer
Vision (WACV), Clearwater, FL, January 2013.
29. J. Kim and K. Grauman. Shape Sharing for Segmentation. In Proceedings of the European
Conference on Computer Vision (ECCV), Florence, Italy, October 2012. (oral presentation,
2.8% acceptance rate)
30. S. Vijayanarasimhan and K. Grauman. Active Frame Selection for Label Propagation in
Videos. In Proceedings of the European Conference on Computer Vision (ECCV), Florence,
Italy, October 2012.
31. S. J. Hwang, K. Grauman, and F. Sha. Semantic Kernel Forests from Multiple Taxonomies. In
Advances in Neural Information Processing Systems (NIPS). Lake Tahoe, Nevada, December
2012.
32. D. Parikh, A. Kovashka, A. Parkash, and K. Grauman. Relative Attributes for Enhanced
Human-Machine Communication. Invited paper, Proceedings of AAAI, Sub-Area Spotlights
Track for Best Papers, Toronto, Canada, July 2012.
33. Y. J. Lee, J. Ghosh, and K. Grauman. Discovering Important People and Objects for Egocentric
Video Summarization. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Providence, RI, June 2012.
34. B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic Flow Kernel for Unsupervised Domain
Adaptation. In Proceedings of the IEEE Conf on Computer Vision and Pattern Recognition
(CVPR), Providence, RI, June 2012. (oral presentation, 2.5% acceptance rate)
35. C.-Y. Chen and K. Grauman. Efficient Activity Detection with Max-Subgraph Search. In
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Providence, RI, June 2012.
36. A. Kovashka, D. Parikh, and K. Grauman. WhittleSearch: Image Search with Relative Attribute
Feedback. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Providence, RI, June 2012.
37. K. Duan, D. Parikh, D. Crandall, and K. Grauman. Discovering Localized Attributes for
Fine-grained Recognition. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Providence, RI, June 2012.
38. D. Parikh and K. Grauman. Relative Attributes. In Proceedings of the International Conference
on Computer Vision (ICCV), Barcelona, Spain, November 2011. (oral presentation,
3% acceptance rate) [Best Paper Award]
39. J. Donahue and K. Grauman. Annotator Rationales for Visual Recognition. In Proceedings of
the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011.
40. A. Kovashka, S. Vijayanarasimhan, and K. Grauman. Actively Selecting Annotations Among
Objects and Attributes. In Proceedings of the International Conference on Computer Vision
(ICCV), Barcelona, Spain, November 2011.
41. Y. J. Lee, J. Kim, and K. Grauman. Key-Segments for Video Object Segmentation. In
Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain,
November 2011.
42. S. J. Hwang, K. Grauman, F. Sha. Learning a Tree of Metrics with Disjoint Visual Features.
In Advances in Neural Information Processing Systems (NIPS). Granada, Spain, December
2011.
43. Y. J. Lee and K. Grauman. Face Discovery with Social Context. In Proceedings of the British
Conference on Computer Vision (BMVC), Dundee, Scotland, August 2011.
44. S. Vijayanarasimhan and K. Grauman. Large-Scale Live Active Learning: Training Object
Detectors with Crawled Data and Crowds. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011.
(oral presentation, 3.5% acceptance rate)
45. J. Kim and K. Grauman. Boundary-Preserving Dense Local Regions. In Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs,
CO, June 2011. (oral presentation, 3.5% acceptance rate)
46. D. Parikh and K. Grauman. Interactively Building a Discriminative Vocabulary of Nameable
Attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Colorado Springs, CO, June 2011.
47. Y. J. Lee and K. Grauman. Learning the Easy Things First: Self-Paced Visual Category Discovery.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Colorado Springs, CO, June 2011.
48. S. J. Hwang, F. Sha, and K. Grauman. Sharing Features Between Objects and Their Attributes.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Colorado Springs, CO, June 2011.
49. S. Vijayanarasimhan and K. Grauman. Efficient Region Search for Object Detection. In
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Colorado Springs, CO, June 2011.
50. C.-Y. Chen and K. Grauman. Clues from the Beaten Path: Location Estimation with Bursty
Sequences of Tourist Photos. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Colorado Springs, CO, June 2011.
51. Z. Kang, K. Grauman, and F. Sha. Learning with Whom to Share in Multi-task Feature Learning.
In Proceedings of the International Conference on Machine Learning (ICML), Bellevue,
WA, July 2011. (oral presentation)
52. P. Jain, S. Vijayanarasimhan, and K. Grauman. Hashing Hyperplane Queries to Near Points
with Applications to Large-Scale Active Learning. In Advances in Neural Information Processing
Systems 23 (NIPS), Vancouver, Canada, December 2010.
53. Y. J. Lee and K. Grauman. Object-Graphs for Context-Aware Category Discovery. In Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San
Francisco, CA, June 2010. (oral presentation, 4% acceptance rate)
54. S. J. Hwang and K. Grauman. Reading Between The Lines: Object Localization Using Implicit
Cues from Image Tags. In Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), San Francisco, CA, June 2010. (oral presentation, 4%
acceptance rate)
55. S. Vijayanarasimhan, P. Jain, and K. Grauman. Far-Sighted Active Learning on a Budget for
Image and Video Recognition. In Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), San Francisco, CA, June 2010.
56. Y. J. Lee and K. Grauman. Collect-Cut: Segmentation with Top-Down Cues Discovered in
Multi-Object Images. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), San Francisco, CA, June 2010.
57. A. Kovashka and K. Grauman. Learning a Hierarchy of Discriminative Space-Time Neighborhood
Features for Human Action Recognition. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010.
58. J. Kim and K. Grauman. Asymmetric Region-to-Image Matching for Comparing Images with
Generic Object Categories. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), San Francisco, CA, June 2010.
59. S. J. Hwang and K. Grauman. Accounting for the Relative Importance of Objects in Image
Retrieval. In Proceedings of the British Machine Vision Conference (BMVC), Aberystwyth,
U.K., September 2010. (oral presentation, 9% acceptance rate)
60. A. Moorthy, A. Mittal, S. Jahanbin, K. Grauman, A. Bovik. 3D Facial Similarity: Automatic
Assessment versus Perceptual Judgments. In IEEE Fourth International Conference on
Biometrics: Theory, Applications and Systems, September 2010.
61. B. Kulis and K. Grauman. Kernelized Locality-Sensitive Hashing for Scalable Image Search.
In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Kyoto,
Japan, October 2009.

Awards

• Presidential Early Career Award for Scientists and Engineers (PECASE), 2014
• Computers and Thought Award, International Joint Conferences on Artificial Intelligence, 2013
• Pattern Analysis and Machine Intelligence (PAMI) Young Researcher Award, 2013
• Alfred P. Sloan Research Fellow, 2012
• Office of Naval Research Young Investigator Research Award (ONR YIP), 2012
• Regents’ Outstanding Teaching Award, University of Texas System, 2012
• Marr Prize, Best Paper Award, International Conference on Computer Vision (ICCV), 2011
For the paper “Relative Attributes”, with D. Parikh.
• Society for Teaching Excellence, University of Texas at Austin, 2011-present
• AI’s Ten to Watch, IEEE Intelligent Systems, 2011
• Best Poster Award, Workshop on Fine-Grained Visual Categorization, 2011
For the work “Interactive Discovery of Task-Specific Nameable Attributes”, with D. Parikh
• Computer Science Study Group, Defense Advanced Research Projects Agency (CSSG), 2010
• Invited research article for the Communications of the ACM (CACM), 2010
Publication for computing and IT professionals with a readership over 95,000
• National Science Foundation Faculty Early Career Development Award (NSF CAREER), 2008
• Microsoft Research New Faculty Fellow, 2008
• Best Student Paper Award, Computer Vision and Pattern Recognition (CVPR), 2008
For the paper “Fast Image Search for Learned Metrics”, with P. Jain and B. Kulis
• Frederick A. Howes Scholar Award in Computational Science, Krell Institute, 2007
• Clare Boothe Luce Assistant Professorship, Henry Luce Foundation, 2007-2011
• Computational Science Graduate Fellowship, Department of Energy, 2001-2005
• Morris Joseph Levin Award, MIT Electrical Engineering and Computer Science Dept., 2003
• Boston College Presidential Scholar, 1997-2001
• Alfred McGuinn Award, for achievement in sciences and humanities, Boston College, 2001
• Accenture Award, Boston College Computer Science Departmental Award, 2001