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UT-Austin
Computer Vision Group Publications [view with images/code/slides] [view by topic] [view by year] |
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| We
are
exploring
problems
in
visual
recognition
and
search.
To
this
end,
we are specifically focused on these topics: |
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| Fast
similarity search and image retrieval |
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| WhittleSearch: Image
Search with Relative Attribute Feedback. A. Kovashka, D. Parikh,
and K. Grauman. To appear, Proceedings of
the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Providence, RI, June 2012. [pdf] [supp]
Learning Binary Hash Codes for Large-Scale Image Search. K. Grauman and R. Fergus. Book chapter, in Machine Learning for Computer Vision, Ed., R. Cipolla, S. Battiato, and G. Farinella, Studies in Computational Intelligence Series, Springer, Volume 411, pp. 49-87, 2013 [pdf] [link] Efficient Region Search for Object Detection. S. Vijayanarasimhan and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf] Kernelized Locality-Sensitive Hashing
for Scalable Image Search. B. Kulis
and K. Grauman. In Proceedings of the IEEE
International Conference on Computer Vision (ICCV),
Kyoto, Japan, October, 2009. [pdf]
Kernelized Locality-Sensitive Hashing. B. Kulis and K. Grauman. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 34, No. 6, June 2012. [link] Learning Binary Hash Codes for Large-Scale Image Search. K. Grauman and R. Fergus. Book chapter, in Machine Learning for Computer Vision, Ed., R. Cipolla, S. Battiato, and G. Farinella, Studies in Computational Intelligence Series, Springer, Volume 411, pp. 49-87, 2013 [pdf] [link] Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning. P. Jain, S. Vijayanarasimhan, and K. Grauman. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2010. [pdf] Fast Similarity Search for
Learned Metrics. B. Kulis, P. Jain, and K.
Grauman. In IEEE Transactions on Pattern
Analysis and Machine Intelligence (TPAMI), Vol. 31,
No. 12, December, 2009. [link] Accounting for the
Relative Importance of Objects in Image
Retrieval. S. J. Hwang and K. Grauman. In
Proceedings of the British Machine Vision Conference (BMVC),
Aberystwyth, UK, September 2010. (Oral) [pdf] Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search. S. J. Hwang and K. Grauman. International Journal of Computer Vision (IJCV), published online October 2011. [link]
Efficiently
Searching for Similar Images.
K. Grauman. Invited article to appear in the
Communications of the ACM, 2009. [pdf] Online
Metric Learning and Fast Similarity Search.
P. Jain, B. Kulis,
I. Dhillon, and K.
Grauman. In Advances in
Neural Information Processing Systems (NIPS),
Vancouver, Canada, December 2008. (Oral) [pdf] Fast
Image Search for Learned Metrics.
P. Jain, B. Kulis,
and K. Grauman. In Proceedings of the IEEE
Conference on Computer Vision and Pattern
Recognition (CVPR), Anchorage, Alaska, June 2008. (Oral) [Best Student Paper
Award] [pdf] Pyramid Match
Hashing: Sub-Linear Time Indexing Over Partial
Correspondences. K.
Grauman and T. Darrell. In Proceedings of the IEEE
Conference on Computer Vision and Pattern
Recognition (CVPR), Minneapolis, MN, June 2007. [pdf] A Picture is Worth a Thousand Keywords:
Image-Based Object Search on a Mobile
Platform. T. Yeh, K. Grauman, K. Tollmar, and T.
Darrell. In CHI 2005, Conference on Human
Factors in Computing Systems, Portland, OR, April 2005. [pdf] |
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| Active and interactive
visual learning, human-in-the-loop |
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Active Frame Selection for Label Propagation in Videos. S. Vijayanarasimhan and K. Grauman. To appear, Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy, October 2012. WhittleSearch: Image Search with Relative Attribute Feedback. A. Kovashka, D. Parikh, and K. Grauman. To appear, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. [pdf] [supp] Annotator Rationales for Visual Recognition. J. Donahue and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. [pdf] Actively Selecting Annotations Among Objects and Attributes. A. Kovashka, S. Vijayanarasimhan, and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. [pdf] Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds. S. Vijayanarasimhan and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. (Oral) [pdf] Interactively Building a Discriminative Vocabulary of Nameable Attributes. D. Parikh and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf] Discovering Localized Attributes for Fine-grained Recognition. K. Duan, D. Parikh, D. Crandall, and K. Grauman. To appear, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. [pdf] Hashing Hyperplane Queries
to Near Points with Applications to Large-Scale Active
Learning. P. Jain, S. Vijayanarasimhan, and K.
Grauman. In Advances
in Neural Information Processing Systems
(NIPS), Vancouver, Canada, December 2010. [pdf]
Far-Sighted Active Learning on a Budget for Image and Video Recognition. S. Vijayanarasimhan, P. Jain, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf] Cost-Sensitive Active Visual Category Learning. S. Vijayanarasimhan and K. Grauman. International Journal of Computer Vision (IJCV), Vol. 91, Issue 1 (2011), p. 24. (online first July 2010). [link] Minimizing Annotation Costs in Visual
Category Learning. S. Vijayanarasimhan
and K. Grauman. Invited chapter, in Cost-Sensitive
Machine Learning, B. Krishnapuram, S. Yu, and B.
Rao, Editors. Chapman and Hall/CRC, December
2011. [link] Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags. S. J. Hwang and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. (Oral) [pdf] Reading Between The Lines: Object Localization
Using Implicit Cues from Image Tags.
S. J. Hwang and K. Grauman.
IEEE
Transactions on Pattern Analysis and Machine
Intelligence (TPAMI), Vol. 34, No. 6, pp. 1145-1158, June 2012. [link] What’s It Going to
Cost You?: Predicting
Effort vs. Informativeness
for Multi-Label Image Annotations.
S. Vijayanarasimhan
and K. Grauman. In Proceedings of the IEEE
Conference on Computer Vision and Pattern
Recognition (CVPR), Miami, FL, June 2009. [pdf] |
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| Unsupervised
and semi-supervised visual discovery |
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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. [pdf]
Learning the Easy Things First: Self-Paced Visual Category Discovery. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf] Object-Graphs for Context-Aware Category Discovery. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. (Oral) [pdf] Object-Graphs for Context-Aware Category Discovery. Y. J. Lee and K. Grauman. In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 34, No. 2, pp. 346-358, February 2012. [link] Collect-Cut: Segmentation with Top-Down Cues Discovered in Multi-Object Images. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf] Face Discovery with Social Context. Y. J. Lee and K. Grauman. In Proceedings of the British Machine Vision Conference (BMVC), Dundee, U.K., August 2011. [pdf] Foreground Focus: Unsupervised Learning from Partially Matching Images. Y. J. Lee and K. Grauman. In International Journal of Computer Vision (IJCV), Vol. 85, No. 2, 2009. [link] Observe Locally, Infer Globally: a Space-Time MRF for Detecting Abnormal Activities with Incremental Updates. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009. [pdf] Shape Discovery from Unlabeled Image Collections. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009. [pdf] Foreground Focus: Finding Meaningful Features in Unlabeled Images. Y. J. Lee and K. Grauman. In Proceedings of the British Machine Vision Conference (BMVC), Leeds, U.K., September 2008. (Oral) [pdf] Masters Thesis: Foreground Focus: Finding Meaningful Features in Unlabeled Images. Y. J. Lee. Thesis, Master of Science in Engineering, August 2008. [pdf] Keywords to Visual Categories: Multiple-Instance Learning for Weakly Supervised Object Categorization. S. Vijayanarasimhan and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008. [pdf] Watch, Listen & Learn: Co-training on Captioned Images and Videos. S. Gupta, J. Kim, K. Grauman, and R. Mooney. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Antwerp, Belgium, September 2008. [pdf] Unsupervised Learning of Categories from Sets of Partially Matching Image Features. K. Grauman and T. Darrell. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York City, NY, June 2006. (Oral) [pdf] |
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| Image
matching and local feature correspondences |
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Deformable Spatial Pyramid Matching for Fast Dense Correspondences. J. Kim, C. Liu, F. Sha, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. [pdf] Boundary-Preserving Dense Local Regions. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. (Oral) [pdf] Asymmetric Region-to-Image Matching for Comparing Images with Generic Object Categories. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf] Clues from the Beaten Path: Location Estimation with Bursty Sequences of Tourist Photos. C.-Y. Chen and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf] 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. (Oral) [pdf] The Pyramid Match:
Efficient Learning with Partial Correspondences. K. Grauman.
In Proceedings of the Association for the
Advancement of Artificial Intelligence (AAAI),
(Nectar track, for AI results presented at other
conferences in last two years), Vancouver, Canada,
July 2007. [pdf] Efficient
Image Matching with Distributions of Local
Invariant Features.
K. Grauman and T. Darrell.
In Proceedings IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), San Diego,
CA, June 2005. [pdf] |
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| Region-based
recognition and segmentation |
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Shape Sharing for Segmentation. J. Kim and K. Grauman. To appear, Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy, October 2012. (Oral) [pdf] [supp] Key-Segments for Video Object Segmentation. Y. J. Lee, J. Kim, and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. [pdf] Efficient Region Search for Object Detection. S. Vijayanarasimhan and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf] Boundary-Preserving Dense Local Regions. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. (Oral) [pdf] Asymmetric Region-to-Image Matching for Comparing Images with Generic Object Categories. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf] Top-Down Pairwise Potentials for Piecing Together Multi-Class Segmentation Puzzles. S. Vijayanarasimhan and K.Grauman. In Proceedings of the Seventh IEEE Computer Society Workshop on Perceptual Organization in Computer Vision (POCV), June 2010. [pdf] Object-Graphs for Context-Aware Category Discovery. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. (Oral) [pdf] Object-Graphs for Context-Aware Category Discovery. Y. J. Lee and K. Grauman. In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2011. [link] Collect-Cut: Segmentation with Top-Down Cues Discovered in Multi-Object Images. Y. J. Lee and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf] |
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| Activity
recognition and video processing |
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Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots. C-Y. Chen and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. (Oral) [pdf] Active Frame Selection for Label Propagation in Videos. S. Vijayanarasimhan and K. Grauman. In Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy, October 2012. Efficient Activity Detection with Max-Subgraph Search. C.-Y. Chen and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. [pdf] Story-Driven Summarization for Egocentric Video. Z. Lu and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. [pdf] 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. [pdf] Key-Segments for Video Object Segmentation. Y. J. Lee, J. Kim, and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. [pdf] Learning
a Hierarchy of Discriminative Space-Time
Neighborhood Features for Human Action
Recognition.
A. Kovashka and
K. Grauman. In Proceedings of the IEEE
Conference on Computer Vision and Pattern
Recognition (CVPR), San Francisco, CA, June
2010. [pdf]
Far-Sighted Active Learning on a Budget for Image and Video Recognition. S. Vijayanarasimhan, P. Jain, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, June 2010. [pdf] Observe Locally, Infer Globally: a Space-Time MRF for Detecting Abnormal Activities with Incremental Updates. J. Kim and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, June 2009. [pdf] Watch, Listen & Learn: Co-training on Captioned Images and Videos. S. Gupta, J. Kim, K. Grauman, and R. Mooney. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Antwerp, Belgium, September 2008. [pdf] A Task-Driven Intelligent Workspace System to Provide Guidance Feedback. M. S. Ryoo, K. Grauman, and J. K. Aggarwal. Computer Vision and Image Understanding, 2010. [link] Communication via Eye Blinks and Eyebrow
Raises: Video-Based Human-Computer Interfaces. K.
Grauman, M. Betke, J.
Lombardi, J. Gips, and
G. Bradski. Universal Access in the
Information Society, 2(4) pp. 359-373, Springer-Verlag Heidelberg, November
2003. [link] Communication via Eye Blinks: Detection and Duration Analysis in Real Time. K. Grauman, M. Betke, J. Gips, and G. Bradski. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Lihue, HI, December 2001. [pdf] |
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| Learning
semantic visual representations |
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Analogy-Preserving Semantic Embedding for Visual Object Categorization. S. J. Hwang, K. Grauman, and F. Sha. In International Conference on Machine Learning (ICML), Atlanta, GA, June 2013. [pdf] Semantic Kernel Forests from Multiple Taxonomies. S. J. Hwang, K. Grauman, and F. Sha. In Advances in Neural Information Processing Systems (NIPS), Tahoe, Nevada, December 2012. [pdf] Semantic Kernel Forests from Multiple Taxonomies. S. J. Hwang, F. Sha, and K. Grauman. In Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval. In conjunction with NIPS, 2012. [pdf] Discovering Localized Attributes for Fine-grained Recognition. K. Duan, D. Parikh, D. Crandall, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. [pdf] Relative Attributes. D. Parikh and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. (Oral) [pdf] [Marr Prize, ICCV Best Paper Award] Relative Attributes for Enhanced Human-Machine Communication. D. Parikh, A. Kovashka, A. Parkash, and K. Grauman. Invited paper, Proceedings of AAAI 2012, Sub-Area Spotlights Track for Best Papers. [pdf] Sharing Features Between Objects and Their Attributes. S. J. Hwang, F. Sha, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011. [pdf] Learning with Whom to Share in Multi-task Feature Learning. Z. Kang, K. Grauman, and F. Sha. In Proceedings of the International Conference on Machine Learning (ICML), Bellevue, WA, July 2011. [pdf] Accounting for the Relative Importance of Objects in Image Retrieval. S. J. Hwang and K. Grauman. In Proceedings of the British Machine Vision Conference (BMVC), Aberystwyth, UK, September 2010. (Oral) [pdf] Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search. S. J. Hwang and K. Grauman. International Journal of Computer Vision (IJCV), Vol. 100, Issue 2, pp. 134-153, November 2012. [link]Learning a Tree of Metrics with Disjoint Visual Features. S. J. Hwang, K. Grauman, F. Sha. In Advances in Neural Information Processing Systems (NIPS). Granada, Spain, December 2011. [pdf] |
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| Other topics |
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Geodesic Flow Kernel for Unsupervised Domain Adaptation. B. Gong, Y. Shi, F. Sha, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. (Oral) [pdf] [supp] Overcoming
Dataset Bias: An Unsupervised Domain Adaptation
Approach. B. Gong, F. Sha, and K. Grauman.
In Big Data Meets Computer Vision: First International
Workshop on Large Scale Visual Recognition and
Retrieval. In conjunction with NIPS, 2012.
(Oral) [pdf]
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. B. Gong, K. Grauman, and F. Sha. In International Conference on Machine Learning (ICML), Atlanta, GA, June 2013. (Oral) [pdf] [supp] Reconstructing
a Fragmented Face from a Cryptographic Identification
Protocol. A. Luong, M. Gerbush, B. Waters, and
K. Grauman. In Proceedings of the IEEE Workshop
on Applications of Computer Vision (WACV), Clearwater
Beach, FL, January 2013. [pdf] Avoiding the ``Streetlight Effect'': Tracking
by Exploring Likelihood Modes.
D. Demirdjian,
L. Taycher, G. Shakhnarovich, K. Grauman,
and T. Darrell. In Proceedings of the IEEE
International Conference on Computer Vision
(ICCV), Beijing, China, October 2005. [pdf] Virtual Visual Hulls: Example-Based 3D Shape
Inference from a Single Silhouette.
K. Grauman, G. Shakhnarovich,
and T. Darrell. In
Proceedings of the 2nd Workshop on Statistical
Methods in Video Processing, in conjunction with
ECCV, Prague, Czech Republic, May 2004. [pdf] A Bayesian Approach
to Image-Based Visual Hull Reconstruction.
K. Grauman, G. Shakhnarovich,
and T. Darrell. In Proceedings of the IEEE
Conference on Computer Vision and Pattern
Recognition (CVPR), Madison, WI, June 2003. [pdf] |