UT-Austin Computer Vision Group Publications by year


	UT-Austin Computer Vision Group Publications [view with images/code/slides] [view by topic] [view by year]

	2013
	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] 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] 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] 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] 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] 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]

	2012
	Shape Sharing for Object Segmentation. J. Kim and K. Grauman. In Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy, October 2012. (Oral) [pdf] [supp] 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. [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] 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. [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] 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] WhittleSearch: Image Search with Relative Attribute Feedback. A. Kovashka, D. Parikh, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012. [pdf] [supp] 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] 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] 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. Toronto, July 2012. [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] 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] 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]

	2011
	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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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]

	2010
	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] 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] 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] 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] 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] 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] 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] 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] 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] 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] Cost-Sensitive Active Visual Category Learning. S. Vijayanarasimhan and K. Grauman. International Journal of Computer Vision (IJCV), Vol. 91, Issue 1 (2011), online first July 2010. [link]

	2009
	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] 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] 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] Gaussian Processes for Object Categorization. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. In International Journal of Computer Vision (IJCV), Vol. 88, No. 2, July 2009. [link] Efficiently Searching for Similar Images. K. Grauman. Invited article to appear in the Communications of the ACM, 2009. [extended version pdf] [CACM version] 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] 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] 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]

	2008
	Multi-Level Active Prediction of Useful Image Annotations for Recognition. S. Vijayanarasimhan and K. Grauman. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2008. (Oral) [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) [pdf] [Best Student Paper Award] 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] 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] 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]

	2007
	Active Learning with Gaussian Processes for Object Categorization. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007. [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] Approximate Correspondences in High Dimensions. K. Grauman and T. Darrell. In Advances in Neural Information Processing Systems 19 (NIPS) 2007. [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, Vancouver, Canada, July 2007. [pdf] The Pyramid Match Kernel: Efficient Learning with Sets of Features. K. Grauman and T. Darrell. Journal of Machine Learning Research (JMLR), 8 (Apr): 725--760, 2007. [pdf]

	2006 and before
	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. [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. [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] 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] 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] Fast Contour Matching Using Approximate Earth Mover's Distance. K. Grauman and T. Darrell. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington DC, June 2004. [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] Inferring 3D Structure with a Statistical Image-Based Shape Model. K. Grauman, G. Shakhnarovich, and T. Darrell. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Nice, France, October 2003. [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] 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]

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]