| Geodesic flow kernel for unsupervised domain adaptation B Gong, Y Shi, F Sha, K Grauman 2012 IEEE conference on computer vision and pattern recognition, 2066-2073, 2012 | 2450 | 2012 |
| The pyramid match kernel: Discriminative classification with sets of image features K Grauman, T Darrell Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 2 …, 2005 | 2110 | 2005 |
| Relative attributes D Parikh, K Grauman 2011 International Conference on Computer Vision, 503-510, 2011 | 1154 | 2011 |
| Kernelized locality-sensitive hashing for scalable image search B Kulis, K Grauman 2009 IEEE 12th international conference on computer vision, 2130-2137, 2009 | 1084 | 2009 |
| Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates J Kim, K Grauman 2009 IEEE conference on computer vision and pattern recognition, 2921-2928, 2009 | 862 | 2009 |
| Discovering important people and objects for egocentric video summarization YJ Lee, J Ghosh, K Grauman 2012 IEEE conference on computer vision and pattern recognition, 1346-1353, 2012 | 816 | 2012 |
| Proceedings of the 32nd International Conference on Neural Information Processing Systems S Bengio, HM Wallach, H Larochelle, K Grauman, N Cesa-Bianchi Curran Associates Inc., 2018 | 710 | 2018 |
| Learning a hierarchy of discriminative space-time neighborhood features for human action recognition A Kovashka, K Grauman 2010 IEEE computer society conference on computer vision and pattern …, 2010 | 680 | 2010 |
| Video Summarization with Long Short-term Memory K Zhang, WL Chao, F Sha, K Grauman Proceedings of the European Conference on Computer Vision (ECCV), 2016 | 654 | 2016 |
| Key-segments for video object segmentation YJ Lee, J Kim, K Grauman 2011 International conference on computer vision, 1995-2002, 2011 | 591 | 2011 |
| Story-Driven Summarization for Egocentric Video Z Lu, K Grauman Conference on Computer Vision and Pattern Recognition (CVPR), 0 | 591* | |
| The pyramid match kernel: Efficient learning with sets of features. K Grauman, T Darrell Journal of Machine Learning Research 8 (4), 2007 | 487 | 2007 |
| Fine-grained visual comparisons with local learning A Yu, K Grauman Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 483 | 2014 |
| Diverse sequential subset selection for supervised video summarization B Gong, WL Chao, K Grauman, F Sha Advances in neural information processing systems 27, 2014 | 471 | 2014 |
| Blockdrop: Dynamic inference paths in residual networks Z Wu, T Nagarajan, A Kumar, S Rennie, LS Davis, K Grauman, R Feris Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 448 | 2018 |
| Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation B Gong, K Grauman, F Sha International conference on machine learning, 222-230, 2013 | 440 | 2013 |
| Active learning with gaussian processes for object categorization A Kapoor, K Grauman, R Urtasun, T Darrell 2007 IEEE 11th international conference on computer vision, 1-8, 2007 | 421 | 2007 |
| Learning with whom to share in multi-task feature learning Z Kang, K Grauman, F Sha Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 415 | 2011 |
| Kernelized locality-sensitive hashing B Kulis, K Grauman IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (6), 1092-1104, 2011 | 411 | 2011 |
| Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds S Vijayanarasimhan, K Grauman Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2011 | 397 | 2011 |
