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Philipp Krähenbühl
Philipp Krähenbühl
UT Austin
Verified email at cs.utexas.edu - Homepage
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
52512016
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
P Krähenbühl, V Koltun
NIPS, 2011
35902011
Objects as points
X Zhou, D Wang, P Krähenbühl
arXiv preprint arXiv:1904.07850, 2019
28122019
Adversarial feature learning
J Donahue, P Krähenbühl, T Darrell
arXiv preprint arXiv:1605.09782, 2016
20312016
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
19892012
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
13792016
Sampling matters in deep embedding learning
CY Wu, R Manmatha, AJ Smola, P Krähenbühl
ICCV 2017, 2017
9022017
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
8862019
Center-based 3d object detection and tracking
T Yin, X Zhou, P Krähenbühl
CVPR, 2021
7272021
Tracking Objects as Points
X Zhou, V Koltun, P Krähenbühl
ECCV, 2020
6992020
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
6902015
Geodesic object proposals
P Krähenbühl, V Koltun
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014
4472014
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
4372019
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
3742016
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
3102018
A system for retargeting of streaming video
P Krähenbühl, M Lang, A Hornung, M Gross
ACM SIGGRAPH Asia 2009 papers, 1-10, 2009
2992009
Video compression through image interpolation
CY Wu, N Singhal, P Krahenbuhl
Proceedings of the European conference on computer vision (ECCV), 416-431, 2018
2792018
Learning by cheating
D Chen, B Zhou, V Koltun, P Krähenbühl
Conference on Robot Learning, 66-75, 2019
2682019
Parameter learning and convergent inference for dense random fields
P Krähenbühl, V Koltun
International Conference on Machine Learning, 513-521, 2013
2502013
Data-dependent initializations of convolutional neural networks
P Krähenbühl, C Doersch, J Donahue, T Darrell
arXiv preprint arXiv:1511.06856, 2015
2312015
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