[view with images/code/slides] [view by topic] [view by year]
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. (Oral) [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] [project page]
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]
Learning a Tree of Metrics with Disjoint Visual Features. S. J. Hwang, K. Grauman, F. Sha. To appear, Advances in Neural Information Processing Systems (NIPS). Granada, Spain, December 2011. [pdf] [poster] [project page]
We show some additional analysis of the annotation collection, to be presented at the Human Computation Workshop (HCOMP), at AAAI 2011. [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] [project page] [poster]
We show some additional results in our short abstract to be presented at the Fine-Grained Visual Categorization Workshop (FGVC) at CVPR 2011. [Best Poster Award] [pdf]
We show some additional results in our short abstract to appear in Fine-Grained Visual Categorization Workshop (FGVC) at CVPR 2011. [pdf] [poster]
Clues from the Beaten Path: Location Estimation with Bursty Sequences of Tourist Photos. C.-Y. Chen. Master's thesis, 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]
[project
page] [slides]
[code]
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. In Proceedings
of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), San Francisco, CA, June 2010.
(Oral) [pdf]
[project
page] [slides]
[data]
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]
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]
[project
page] [code]
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]
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]
[project
page] [poster]
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]
[project
page] [poster]
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]
[project
page] [code]
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]
[poster]
[code]
[project
page]
Kernelized Locality-Sensitive
Hashing. B. Kulis and K. Grauman. IEEE Transactions on Pattern Analysis
and Machine Intelligence (TPAMI), Vol. 34, No. 6, pp. 1092-1104, June
2012. [link]
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]
[project
page] [poster]
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, Dec.
2008. (Oral) [pdf] [slides]
[project
page]
Cost-Sensitive
Active Visual Category Learning. S.
Vijayanarasimhan and K. Grauman. Abstract
presented
at the Learning Workshop, Clearwater FL, April 2009. [abstract]
[slides]
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]
[project
page]
Efficiently
Searching for Similar Images. K.
Grauman. Invited article to
appear in the Communications of the ACM,
2009. [pre-print]
[CACM
link]
Online Metric
Learning and Fast Similarity Search.
P. Jain, B. Kulis,
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),
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]
[project
page]
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]
[project
page] [loopy
BP code]
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]
[project
page]
Foreground
Focus: Finding Meaningful Features in Unlabeled Images.
Y. J. Lee and K. Grauman. In
Proceedings of the British Machine Vision Conference
(BMVC),
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),
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),
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]
Gaussian Processes for Object Categorization. A. Kapoor, K. Grauman, R. Uratsun, and T. Darrell. In International Journal of Computer Vision (IJCV), Vol. 88, No. 2, 2010. [link]
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),
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)
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] [code] [project
page]
Matching Sets of Features for Efficient Retrieval and
Recognition, K. Grauman, Ph.D. Thesis,
MIT, 2006. [pdf]
(35.8 MB)
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),
Approximate
Correspondences in High Dimensions.
K.
Grauman and T. Darrell. In Advances in Neural
Information Processing Systems 19 (NIPS) 2007. [pdf] [code] [poster
(pdf)] [poster
(ppt)]
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),
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),
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),
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,
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,
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),
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),
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]
[project
page]
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
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),