Philipp Krähenbühl

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Vladlen KoltunDistinguished Scientist, AppleVerified email at apple.com
Xingyi ZhouGoogle ResearchVerified email at utexas.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
Alexei A. EfrosUC BerkeleyVerified email at eecs.berkeley.edu
Chao-Yuan WuResearch Scientist, Facebook AI Research (FAIR)Verified email at cs.utexas.edu
Alexander Sorkine-HornungMetaVerified email at sorkine-hornung.com
Dequan WangShanghai Jiao Tong University | Shanghai AI LaboratoryVerified email at sjtu.edu.cn
Jeff DonahueResearch Scientist, DeepMindVerified email at google.com
Deepak PathakAssistant Professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Yael PritchGoogle ResearchVerified email at google.com
Federico PerazziBending SpoonsVerified email at bendingspoons.com
Brady ZhouUT AustinVerified email at utexas.edu
Eli ShechtmanAdobe ResearchVerified email at adobe.com
Jun-Yan ZhuAssistant Professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Tianwei YinMITVerified email at mit.edu
Alex SmolaBoson AIVerified email at smola.org
R. ManmathaA9/AmazonVerified email at cs.umass.edu
Markus GrossETH Zurich, Disney Research ZurichVerified email at inf.ethz.ch
Manuel LangCode LaneVerified email at codelane.ch
Dian ChenGraduate Student, UT AustinVerified email at cs.utexas.edu

Philipp Krähenbühl

UT Austin

Verified email at cs.utexas.edu - Homepage

Computer Vision Machine Learning Computer Graphics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Context encoders: Feature learning by inpainting D Pathak, P Krahenbuhl, J Donahue, T Darrell, AA Efros Proceedings of the IEEE conference on computer vision and pattern …, 2016	5205	2016
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials P Krähenbühl, V Koltun NIPS, 2011	3569	2011
Objects as points X Zhou, D Wang, P Krähenbühl arXiv preprint arXiv:1904.07850, 2019	2778	2019
Adversarial feature learning J Donahue, P Krähenbühl, T Darrell arXiv preprint arXiv:1605.09782, 2016	2020	2016
Saliency filters: Contrast based filtering for salient region detection F Perazzi, P Krähenbühl, Y Pritch, A Hornung 2012 IEEE conference on computer vision and pattern recognition, 733-740, 2012	1976	2012
Generative visual manipulation on the natural image manifold JY Zhu, P Krähenbühl, E Shechtman, AA Efros Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016	1373	2016
Sampling matters in deep embedding learning CY Wu, R Manmatha, AJ Smola, P Krähenbühl ICCV 2017, 2017	897	2017
Bottom-up object detection by grouping extreme and center points X Zhou, J Zhuo, P Krahenbuhl Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019	881	2019
Center-based 3d object detection and tracking T Yin, X Zhou, P Krähenbühl CVPR, 2021	709	2021
Constrained convolutional neural networks for weakly supervised segmentation D Pathak, P Krahenbuhl, T Darrell Proceedings of the IEEE international conference on computer vision, 1796-1804, 2015	688	2015
Tracking Objects as Points X Zhou, V Koltun, P Krähenbühl ECCV, 2020	681	2020
Geodesic object proposals P Krähenbühl, V Koltun Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014	446	2014
Long-term feature banks for detailed video understanding CY Wu, C Feichtenhofer, H Fan, K He, P Krahenbuhl, R Girshick Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	434	2019
Learning dense correspondence via 3d-guided cycle consistency T Zhou, P Krahenbuhl, M Aubry, Q Huang, AA Efros Proceedings of the IEEE conference on computer vision and pattern …, 2016	372	2016
Compressed video action recognition CY Wu, M Zaheer, H Hu, R Manmatha, AJ Smola, P Krähenbühl Proceedings of the IEEE conference on computer vision and pattern …, 2018	308	2018
A system for retargeting of streaming video P Krähenbühl, M Lang, A Hornung, M Gross ACM SIGGRAPH Asia 2009 papers, 1-10, 2009	299	2009
Video compression through image interpolation CY Wu, N Singhal, P Krahenbuhl Proceedings of the European conference on computer vision (ECCV), 416-431, 2018	278	2018
Learning by cheating D Chen, B Zhou, V Koltun, P Krähenbühl Conference on Robot Learning, 66-75, 2019	266	2019
Parameter learning and convergent inference for dense random fields P Krähenbühl, V Koltun International Conference on Machine Learning, 513-521, 2013	249	2013
Data-dependent initializations of convolutional neural networks P Krähenbühl, C Doersch, J Donahue, T Darrell arXiv preprint arXiv:1511.06856, 2015	229	2015

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