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Philipp Krähenbühl
Philipp Krähenbühl
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
61502016
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
P Krähenbühl, V Koltun
NIPS, 2011
39742011
Objects as points
X Zhou, D Wang, P Krähenbühl
arXiv preprint arXiv:1904.07850, 2019
38862019
Adversarial feature learning
J Donahue, P Krähenbühl, T Darrell
arXiv preprint arXiv:1605.09782, 2016
23752016
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
21932012
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
15442016
Center-based 3d object detection and tracking
T Yin, X Zhou, P Krähenbühl
CVPR, 2021
13202021
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
10932019
Tracking Objects as Points
X Zhou, V Koltun, P Krähenbühl
ECCV, 2020
10492020
Sampling matters in deep embedding learning
CY Wu, R Manmatha, AJ Smola, P Krähenbühl
ICCV 2017, 2017
10312017
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
7522015
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
5292019
Geodesic object proposals
P Krähenbühl, V Koltun
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014
4642014
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
4182016
Detecting twenty-thousand classes using image-level supervision
X Zhou, R Girdhar, A Joulin, P Krähenbühl, I Misra
European Conference on Computer Vision, 350-368, 2022
3962022
Learning by cheating
D Chen, B Zhou, V Koltun, P Krähenbühl
Conference on Robot Learning, 66-75, 2019
3942019
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
3822018
Video compression through image interpolation
CY Wu, N Singhal, P Krahenbuhl
Proceedings of the European conference on computer vision (ECCV), 416-431, 2018
3452018
Objects as points. arXiv 2019
X Zhou, D Wang, P Krähenbühl
arXiv preprint arXiv:1904.07850 448, 1904
3151904
A system for retargeting of streaming video
P Krähenbühl, M Lang, A Hornung, M Gross
ACM SIGGRAPH Asia 2009 papers, 1-10, 2009
3062009
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