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UT-Austin
Computer Vision Group Publications [view with images/code/slides] [view by topic] [view by year] [student theses] |
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We
are
exploring
problems
in
visual
recognition
and
search.
To
this
end,
we are exploring these topics:
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| Image
search and large-scale retrieval |
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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] |
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| Active and interactive
visual learning, human-in-the-loop |
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ClickCarving: Interactive Object Segmentation in Images and Videos with Point Clicks. S. Jain and K. Grauman. International Journal of Computer Vision (IJCV), Issue 9, 2019. [link] Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch. D. Gurari, Y. Zhao, S. Jain, M. Betke, and K. Grauman. International Journal of Computer Vision (IJCV), Mar 2019. [link] [arXiv] Thinking Outside the Pool: Active Training Image Creation for Relative Attributes. A. Yu and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019. [pdf] [supp] [code/data] Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s). D. Gurari, K. He, B. Xiong, J. Zhang, M. Sameki, S. Jain, S. Sclaroff, M. Betke, and K. Grauman. International Journal of Computer Vision (IJCV), July 2018. [link] 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] |
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| Unsupervised
and semi-supervised visual discovery |
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Co-Separating Sounds of Visual Objects. R. Gao and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea, Nov 2019. [pdf] [supp] Learning to Separate Object Sounds by Watching Unlabeled Video. R. Gao, R. Feris, and K. Grauman. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, Sept 2018. (Oral) [pdf] [videos] 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] |
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| Image
matching and local feature correspondences |
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Single-Stage Visual Query Localization in Egocentric Videos. Hanwen Jiang, Santhosh Kumar Ramakrishnan, and Kristen Grauman. NeurIPS 2023. [pdf] 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] 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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ClickCarving: Interactive Object Segmentation in Images and Videos with Point Clicks. S. Jain and K. Grauman. International Journal of Computer Vision (IJCV), Issue 9, 2019. [link] Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch. D. Gurari, Y. Zhao, S. Jain, M. Betke, and K. Grauman. International Journal of Computer Vision (IJCV), Mar 2019. [link] Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos. B. Xiong, S. Jain, and K. Grauman. To appear, Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2018. [code-imgs] [code-video] Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s). D. Gurari, K. He, B. Xiong, J. Zhang, M. Sameki, S. Jain, S. Sclaroff, M. Betke, and K. Grauman. International Journal of Computer Vision (IJCV), 2018. [link] 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] |
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| Activity
recognition and video understanding |
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Learning Object State Changes in Videos: An Open-World Perspective. Zihui Xue, Kumar Ashutosh, Kristen Grauman. CVPR 2024. [pdf] [project page] Detours for Navigating Instructional Videos. Kumar Ashutosh, Zihui Xue, Tushar Nagarajan, Kristen Grauman. CVPR 2024 (Poster highlight) [pdf] Video-Mined Task Graphs for Keystep Recognition in Instructional Videos. Kumar Ashutosh, Santhosh Kumar Ramakrishnan, Triantafyllos Afouras, Kristen Grauman. NeurIPS 2023. [pdf] EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding. Shuhan Tan, Tushar Nagarajan, Kristen Grauman. NeurIPS 2023 [pdf] What You Say Is What You Show: Visual Narration Detection in Instructional Videos. Kumar Ashutosh, Rohit Girdhar, Lorenzo Torresani, Kristen Grauman. arXiv 2023. [pdf] Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment. Zihui Xue and Kristen Grauman. NeurIPS 2023. [pdf] SpotEM: Efficient Video Search for Episodic Memory. Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman. ICML 2023 [pdf] [project] HierVL: Learning Hierarchical Video-Language Embeddings. Kumar Ashutosh, Rohit Girdhar, Lorenzo Torresani, Kristen Grauman. CVPR 2023. [pdf] [project] Ego4D: Around the World in 3,000 Hours of Egocentric Video. K Grauman et al. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (Oral) [pdf] [supp] [project page] Egocentric Activity Recognition and Localization on a 3D Map. M. Liu, L. Ma, K. Somasundaram, Y. Li, K. Grauman, J. Rehg, C. Li. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. [pdf] [project page] Anticipative Video Transformer. R. Girdhar and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Oct 2021. [pdf] Winner of the EPIC-Kitchens CVPR'21 Action Anticipation Challenge Multiview Pseudo-Labeling for Semi-supervised Learning from Video. B. Xiong, H. Fan, K. Grauman, C. Feichtenhofer. In Proceedings of the International Conference on Computer Vision (ICCV), Oct 2021. Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos. Y. Li, T. Nagarajan, B. Xiong, K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [pdf] Proposal-based Video Completion. Y-T. Hu, H. Wang, N. Ballas, K. Grauman, A. Schwing. In Proceedings of the European Conference on Computer Vision (ECCV), August 2020. Ego-Topo: Environment Affordances from Egocentric Video. T. Nagarajan, Y. Li, C. Feichtenhofer, K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, June 2020. (Oral) [project page/dataset] [pdf] [supp] You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions. E. Ng, D. Xiang, H. Joo, K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, June 2020. (Oral) [project page/dataset] [pdf] Listen to Look: Action Recognition by Previewing Audio. R. Gao, T-H. Oh, K. Grauman, L. Torresani. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, June 2020. [pdf] Learning Compressible 360 Video Isomers. Y-C. Su and K. Grauman. Transactions on Pattern Analysis and Machine Intelligence (PAMI). Feb 2020. [link] Grounded Human-Object Interaction Hotspots from Video. T. Nagarajan, C. Feichtenhofer, K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea, Nov 2019. [pdf] [supp] Co-Separating Sounds of Visual Objects. R. Gao and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea, Nov 2019. [pdf] [supp] Learning to Separate Object Sounds by Watching Unlabeled Video. R. Gao, R. Feris, K. Grauman. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, Sept 2018. (Oral) [pdf] [videos] Im2Flow: Motion Hallucination from Static Images for Action Recognition. R. Gao, B. Xiong, and K. Grauman. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, June 2018. (Oral) [pdf] Learning Compressible 360 Video Isomers. Y-C. Su and K. Grauman. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, June 2018. [pdf] [supp] [data] 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] Predicting the Location of "Interactees" in Novel Human-Object Interactions. C-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 perception / first-person vision
/ embodied AI |
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|
|
| Learning
semantic visual representations / visual attributes |
|
|
Densifying Supervision for Fine-Grained Comparisons. A. Yu and K. Grauman. International Journal of Computer Vision (IJCV), Special Issue on Generative Adversarial Networks for Computer Vision, 2020. [pdf] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias. K. Singh, D. Mahajan, K. Grauman, Y J. Lee, M. Feiszli, D. Ghadiyaram. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, June 2020. (Oral) [pdf] Thinking Outside the Pool: Active Training Image Creation for Relative Attributes. A. Yu and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019. [pdf] [supp] [code/data] Attributes as Operators. T. Nagarajan and K. Grauman. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, Sept 2018. [pdf] [supp] [code] Compare and Contrast: Learning Prominent Visual Differences. S. Chen and K. Grauman. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, June 2018. [pdf] [supp] 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] |
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| Self-supervised
feature learning from video |
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|
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| Audio-visual
video analysis |
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|
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| Domain
adaptation and transfer learning |
|
SpotTune: Transfer Learning through Adaptive Fine-tuning. Y. Guo, H. Shi, A. Kumar, K. Grauman, T. Rosing, and R. Feris. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019. [pdf] 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] 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 |
|
Less is More: Learning Highlight Detection from Video Duration. B. Xiong, Y. Kalantidis, D. Ghadiyaram, and K. Grauman. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019. [pdf] [supp] [videos] |
|
| Fashion image analysis | |
From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images. W-L. Hsiao and K. Grauman. In Proceedings of the International Conference on Computer Vision (ICCV), Oct 2021 (oral). [pdf] [project page] |
|
| 360 Images and Video |
|
|
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| Vision and language |
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What You Say Is What You Show: Visual Narration Detection in Instructional Videos. Kumar Ashutosh, Rohit Girdhar, Lorenzo Torresani, Kristen Grauman. arXiv 2023. [pdf] |
|
| Other topics |
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| |
Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion. Z. Yang, J. Pan, L. Luo, X. Zhou, K. Grauman, and Q. Huang. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019. (Oral) [pdf] [supp] [code] VizWiz
Grand
Challenge:
Answering
Visual
Questions from
Blind
People.
D.
Gurari, Q. Li,
A. Stangl, A.
Guo, C. Lin,
K. Grauman, J.
Luo, and J.
Bigham.
In Proceedings
of IEEE
Conference on
Computer
Vision and
Pattern
Recognition
(CVPR), Salt
Lake City,
June
2018. (Spotlight)
[pdf]
[supp] Visual
Question Answer Diversity. C-J. Yang, K. Grauman,
and D. Gurari. In Proceedings of the Sixth AAAI
Conference on Human Computation and
Crowdsourcing (HCOMP), Zurich, July
2018. [pdf] BlockDrop: Dynamic Inference Paths in Residual Networks. Z. Wu, T. Nagarajan, A. Kumar, S. Rennie, L. Davis, K. Grauman, R. Feris. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, June 2018. (Spotlight) [pdf] [supp] [code] 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] 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] |