UT-Austin Computer Vision Group Publications

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We are exploring problems in visual recognition and search.  To this end, we are specifically focused on these topics:




Image search and large-scale retrieval


WhittleSearch: Interactive Image Search with Relative Attribute Feedback.  A. Kovashka, D. Parikh, and K. Grauman.  International Journal on Computer Vision (IJCV), Volume 115, Issue 2, pp 185-210, November 2015.  [link]  [arxiv]

Attribute Pivots for Guiding Relevance Feedback in Image Search.  A. Kovashka and K. Grauman.  
In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]
[patented]

Attribute Adaptation for Personalized Image Search.  A. Kovashka and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]

Implied Feedback: Learning Nuances of User Behavior in Image Search.  D. Parikh and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [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 [patented]

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]


Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning.  S. Vijayanarasimhan, P. Jain, and K. Grauman.  Transactions on Pattern Analysis and Machine Intelligence (PAMI), Volume 36, No. 2, pp. 276-288, February 2014.

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 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]




Active and interactive visual learning, human-in-the-loop


CrowdVerge: Predicting If People Will Agree on the Answer to a Visual Question.  D. Gurari and K. Grauman. ACM Conference on Human Factors in Computing Systems (CHI), Denver, CO, May 2017.  Honorable Mention Award [pdf

Crowdsourcing in Computer Vision.  A. Kovashka, O. Russakovsky, L. Fei-Fei, and K. Grauman.  Foundations and Trends in Computer Graphics and Vision, Nov 2016.  [link] [arxiv]
[pdf]

Click Carving: Segmenting Objects in Video with Point Clicks. 
S. D. Jain and K. Grauman.  In Proceedings of the Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Austin, TX, October 2016. 
[pdf]

Active Image Segmentation Propagation.  S. D. Jain and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  [pdf]

Pull the Plug?  Predicting If Computers or Humans Should Segment Images.  D. Gurari, S. Jain, M. Betke, and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  [pdf]  [supp]

WhittleSearch: Interactive Image Search with Relative Attribute Feedback.  A. Kovashka, D. Parikh, and K. Grauman.  International Journal on Computer Vision (IJCV), Volume 115, Issue 2, pp 185-210, November 2015.  [link]  [arxiv]

Zero-shot Recognition with Unreliable Attributes
.  D. Jayaraman and K. Grauman.  In Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.  [pdf]  [supp]

Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes.  L. Liang and K. Grauman. 
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014. 
[pdf]

Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation.  S. Jain and K. Grauman. 
In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]

Active Learning of an Action Detector from Untrimmed Videos.  S. Bandla and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]

Attribute Pivots for Guiding Relevance Feedback in Image Search.  A. Kovashka and K. Grauman.  
In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]
[patented]

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.

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 [patented]

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]

Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds.  S. Vijayanarasimhan and K. Grauman.  International Journal of Computer Vision (IJCV), Volume 108, Issue 1-2, pp. 97-114, May 2014. [link]

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.  In 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]

Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning.  S. Vijayanarasimhan, P. Jain, and K. Grauman.  Transactions on Pattern Analysis and Machine Intelligence (PAMI), Volume 36, No. 2, pp. 276-288, February 2014.
 
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]

Cost-Sensitive Active Visual Category Learning.  S. Vijayanarasimhan and K. Grauman.  Abstract in the Learning Workshop (The Snowbird Workshop), Clearwater, FL, April 2009.  [pdf]

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]

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, 2010.  [link]

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]




Unsupervised and semi-supervised visual discovery


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]

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]



Image matching and local feature correspondences


Boundary Preserving Dense Local Regions.  J. Kim and K. Grauman.  IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015.  [link] 

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]

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, for AI results presented at other conferences in last two years), 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]

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]

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]




Region-based recognition and segmentation


Pixel Objectness.  S. Jain, B. Xiong, and K. Grauman.  arXiv.   Jan 2017 patent pending

FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Video.  S. Jain, B. Xiong, and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, July 2017.  [pdf[DAVIS results leaderboard]   patent pending

Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly.  H. Jiang and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, July 2017.  (Oral)  [pdf]

Click Carving: Segmenting Objects in Video with Point Clicks.  S. D. Jain and K. Grauman.  In Proceedings of the Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Austin, TX, October 2016. [pdf] 

Pull the Plug?  Predicting If Computers or Humans Should Segment Images.  D. Gurari, S. Jain, M. Betke, and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  [pdf]  [supp]


Active Image Segmentation Propagation.  S. Jain and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  [pdf]

Which Image Pairs Will Cosegment Well?  Predicting Partners for Cosegmentation.  S. Jain and K. Grauman.  In Proceedings of the Asian Conference on Computer Vision (ACCV), Singapore, Nov 2014.  [pdf]

Supervoxel-Consistent Foreground Propagation in Video.  S. Jain and K. Grauman.  In Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept 2014. 
  [pdf]

Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation.  S. Jain and K. Grauman. 
In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]

Shape Sharing for Segmentation. 
J. Kim and K. Grauman.  In 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.  IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015.
 
[link]   

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]



Activity recognition and video processing


Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video.  H. Jiang and K. Grauman.  To appear,
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, July 2017.  (Spotlight) 
[pdf]

Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video.  Y-C. Su and K. Grauman.  Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, October 2016.  [pdf]  [supp]

Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video.  D. Jayaraman and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  (Spotlight)  [pdf]

Click Carving: Segmenting Objects in Video with Point Clicks.  S. D. Jain and K. Grauman.  In Proceedings of the Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Austin, TX, October 2016.  [pdf]

Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search.  C-Y. Chen and K. Grauman.  IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), April 2016.

Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance.  C-Y. Chen and K. Grauman.  International Journal of Computer Vision (IJCV), Oct 2016.  [link] [arxiv version]
 
P
redicting the Location of "Interactees" in Novel Human-Object InteractionsC-Y. Chen and K. Grauman.  In Proceedings of the Asian Conference on Computer Vision (ACCV), Singapore, Nov 2014.  [pdf

Inferring Unseen Views of People.  C.-Y. Chen and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.  [pdf]

Supervoxel-Consistent Foreground Propagation in Video.  S. Jain and K. Grauman.  In Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept 2014.    [pdf]

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 Learning of an Action Detector from Untrimmed Videos.  S. Bandla and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [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]

Object-Centric Spatio-Temporal Pyramids for Egocentric Activity Recognition.  T. McCandless and K. Grauman.  In Proceedings of the British Machine Vision Conference (BMVC), Bristol, UK, September 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]



Egocentric / first-person computer vision


Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video.  H. Jiang and K. Grauman.  To appear, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, July 2017.  (Spotlight)  [pdf]

Next-active-object prediction from egocentric videos.  A. Furnari, S. Battiato, K. Grauman, and G. Maria Farinella.  Journal of Visual Communication and Image Representation.  Volume 49, pp. 401-411, November 2017.  [link]
 
Learning Image Representations Tied to Egomotion from Unlabeled Video. D. Jayaraman and K. Grauman.  International Journal of Computer Vision (IJCV), Special Issue for Best Papers of ICCV 2015, accepted Feb 2017.  [pdf] [preprint]
 
Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion.  D. Jayaraman and K. Grauman.  Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, October 2016. (Oral)  [pdf]  [supp]
 

Detecting Engagement in Egocentric Video.  Y-C. Su and K. Grauman.  Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, October 2016.  (Oral)  [pdf]  [supp]

Text Detection in Stores Using a Repetition Prior.  B. Xiong and K. Grauman.  In Proceedings of the IEEE Winter Conference on Computer Vision (WACV).  Lake Placid, NY, March 2016.  [pdf]

Intentional Photos from an Unintentional Photographer: Detecting Snap Points in Egocentric Video with a Web Photo Prior.  B. Xiong and K. Grauman.  Invited chapter.  In Mobile Cloud Visual Media Computing.  Springer International Publishing.  Editors: G. Hua and X.-S. Hua.  pp 85-111.  November 2015.  [pdf]

Learning Image Representations Tied to Ego-Motion.  D. Jayaraman and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec 2015.  (Oral)  [pdf]  [supp] 


Predicting Important Objects for Egocentric Video Summarization.  Y. J. Lee and K. Grauman.  International Journal on Computer Vision, V
olume 114, Issue 1, pp. 38-55, August 2015.  [link
[arxiv]

Detecting Snap Points in Egocentric Video with a Web Photo Prior.  B. Xiong and K. Grauman.  In Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept 2014. 
[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]
 

Object-Centric Spatio-Temporal Pyramids for Egocentric Activity Recognition.  T. McCandless and K. Grauman.  In Proceedings of the British Machine Vision Conference (BMVC), Bristol, UK, September 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]




Learning semantic visual representations


Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images.  A. Yu and K. Grauman.  In Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy, Oct 2017.  [pdf]  [supp]

Fine-Grained Comparisons with Attributes. 
A. Yu and K. Grauman.  Chapter in Visual Attributes.  R. Feris, C. Lampert, and D. Parikh, Editors.  Springer.  2017.  [pdf]

Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing.  C-Y. Chen, Dinesh Jayaraman, F. Sha, and K. Grauman.  Chapter in Visual Attributes.  R. Feris, C. Lampert, and D. Parikh, Editors.  Springer.  2017. [pdf]

Attributes for Image Retrieval.  A. Kovashka and K. Grauman. Chapter in Visual Attributes.  R. Feris, C. Lampert, and D. Parikh, Editors.  Springer.  2017.

Just Noticeable Differences in Visual Attributes.  A. Yu and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec 2015.  [pdf]  [supp]


Zero-shot Recognition with Unreliable Attributes.  D. Jayaraman and K. Grauman.  In Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.  [pdf]  [supp]

Predicting Useful Neighborhoods for Lazy Local Learning.  A. Yu and K. Grauman.  In Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.  [pdf] [supp]

Discovering Attribute Shades of Meaning with the Crowd.  A. Kovashka and K. Grauman.  International Journal on Computer Vision (IJCV), Volume 114, Issue 1, pp. 56-73, August 2015. [link] [arxiv]

Discovering Shades of Attribute Meaning with the Crowd.  A. Kovashka and K. Grauman.  Third International Workshop on Parts and Attributes, in conjunction with the European Conference on Computer Vision.  Zurich, Switzerland, Sept 2014.  [pdf]

Fine-Grained Visual Comparisons with Local Learning.  A. Yu and K. Grauman. 
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014. 
[pdf]

Decorrelating Semantic Visual Attributes by Resisting the Urge to Share.  D. Jayaraman, F. Sha, and K. Grauman. 
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.  (Oral)
[pdf]

Inferring Analogous Attributes.  C.-Y. Chen and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.  [pdf]

Attribute Adaptation for Personalized Image Search.  A. Kovashka and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 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]
 

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]




Learning representations from video


Learning Image Representations Tied to Egomotion from Unlabeled Video. D. Jayaraman and K. Grauman.  International Journal of Computer Vision (IJCV), Special Issue for Best Papers of ICCV 2015, accepted Feb 2017.  [pdf] [preprint]

Object-Centric Representation Learning from Unlabeled Videos.  R. Gao, D. Jayaraman, and K. Grauman.  Proceedings of the Asian Conference on Computer Vision (ACCV), Taipei, November 2016.
[pdf]

Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video.  D. Jayaraman and K. Grauman. 
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  (Spotlight)  [pdf]

Learning Image Representations Tied to Ego-Motion.  D. Jayaraman and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec 2015.  (Oral)  [pdf]  [supp



Domain adaptation and transfer learning


Inferring Unseen Views of People.  C.-Y. Chen and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.  [pdf]

Inferring Analogous Attributes.  C.-Y. Chen and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014. 
[pdf]

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition.  B. Gong, K. Grauman, and F. Sha.  International Journal of Computer Vision (IJCV), Volume 109, Issue 1-2, pp. 3-27, August 2014. 
[link]

Reshaping Visual Datasets for Domain Adaptation.  B. Gong, K. Grauman, and F. Sha.  In Proceedings of Advances in Neural Information Processing Systems (NIPS), Tahoe, Nevada, December 2013.  [pdf]


Attribute Adaptation for Personalized Image Search.  A. Kovashka and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [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.  (Oral)  [pdf]

Connecting the Dots with Landmarks:  Discriminatively Learning Domain-Invariant Features for Unsupervised Domain AdaptationB. Gong, K. Grauman, and F. ShaIn International Conference on Machine Learning (ICML), Atlanta, GA, June 2013.  (Oral) [pdf]  [supp]

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]




Video summarization

Making 360 Video Watchable in 2D: Learning Videography for Click Free Viewing.  Y-C. Su and K. Grauman.  To appear, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, July 2017.  (Spotlight)  [pdf]

Pano2Vid: Automatic Cinematography for Watching 360◦ Videos.  Y-C. Su, D. Jayaraman, and K. Grauman.  Proceedings of the Asian Conference on Computer Vision (ACCV), Taipei, November 2016.  (Oral, Best Application Paper Award[pdf]  [supp]

Video Summarization with Long Short-term Memory.  K. Zhang, W-L. Chao, F. Sha, and K. Grauman.  Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, October 2016. 
[pdf]  [supp]

Detecting Engagement in Egocentric Video.  Y-C. Su and K. Grauman.  Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, October 2016.  (Oral)  [pdf]  [supp]

Summary Transfer: Exemplar-based Subset Selection for Video Summarization.  K. Zhang, W-L. Chao, F. Sha, and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.  [pdf] [supp]

Large-Margin Determinantal Point Processes.  W-L. Chao, B. Gong, K. Grauman, and F. Sha. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam, Netherlands, July 2015.  [pdf]  [supp]

Intentional Photos from an Unintentional Photographer: Detecting Snap Points in Egocentric Video with a Web Photo Prior.  B. Xiong and K. Grauman.  Invited chapter.  In Mobile Cloud Visual Media Computing.  Springer International Publishing.  Editors: G. Hua and X.-S. Hua.  pp 85-111.  November 2015.  [pdf]

Predicting Important Objects for Egocentric Video Summarization.  Y J. Lee and K. Grauman.  International Journal on Computer Vision (IJCV).  Volume 114, Issue 1, pp. 38-55, August 2015. [link[arxiv]

Diverse Sequential Subset Selection for Supervised Video Summarization
.  B. Gong, W. Chao, K. Grauman, and F. Sha. 
In Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.  [pdf]


Detecting Snap Points in Egocentric Video with a Web Photo Prior.  B. Xiong and K. Grauman.  In Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept 2014.  [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]



Fashion image analysis

Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images.  W-L. Hsiao and K. Grauman.  In Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy, Oct 2017.  [pdf]

Fashion Forward: Forecasting Visual Style in Fashion.  Z. Al-Halah, R. Stiefelhagen, and K. Grauman.  In Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy, Oct 2017.  [pdf]  [supp]

WhittleSearch: Interactive Image Search with Relative Attribute Feedback.  A. Kovashka, D. Parikh, and K. Grauman.  International Journal on Computer Vision (IJCV), Volume 115, Issue 2, pp 185-210, November 2015.  [link]  [arxiv]

Just Noticeable Differences in Visual Attributes.  A. Yu and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec 2015.  [pdf]  [supp]

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 [patented]

Attribute Pivots for Guiding Relevance Feedback in Image Search.  A. Kovashka and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf] [patented]

Fine-Grained Visual Comparisons with Local Learning.  A. Yu and K. Grauman.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.  [pdf]

Attribute Adaptation for Personalized Image Search.  A. Kovashka and K. Grauman.  In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013.  [pdf]

Discovering Attribute Shades of Meaning with the Crowd.  A. Kovashka and K. Grauman.  International Journal on Computer Vision (IJCV), Volume 114, Issue 1, pp. 56-73, August 2015. [link] [arxiv]



Other topics

Learning Spherical Convolution for Fast Features from 360° Imagery.  Y-C. Su and K. Grauman.  In Advances in Neural Information Processing (NIPS), Long Beach, CA, Dec 2017.  [pdf]  [supp]

On-Demand Learning for Deep Image Restoration.  R. Gao and K. Grauman.  In Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy, Oct 2017.  [pdf]

CrowdVerge: Predicting If People Will Agree on the Answer to a Visual Question.  D. Gurari and K. Grauman. ACM Conference on Human Factors in Computing Systems (CHI), Denver, CO, May 2017.  Best Paper Honorable Mention Award [pdf

Text Detection in Stores Using a Repetition Prior.  B. Xiong and K. Grauman.  In Proceedings of the IEEE Winter Conference on Computer Vision (WACV).  Lake Placid, NY, March 2016.  [pdf]

Predicting Useful Neighborhoods for Lazy Local Learning.  A. Yu and K. Grauman.  In Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2014.  [pdf]  [supp]

Visual Object Recognition, Kristen Grauman and Bastian Leibe, Synthesis Lectures on Artificial Intelligence and Machine Learning, April 2011, Vol. 5, No. 2, Pages 1-181.  [link]

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]

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]