Philipp Krähenbühl
Philipp Krähenbühl
Bestätigte E-Mail-Adresse bei cs.utexas.edu - Startseite
TitelZitiert vonJahr
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
P Krähenbühl, V Koltun
NIPS, 2011
16032011
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
11632012
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
11052016
Adversarial feature learning
J Donahue, P Krähenbühl, T Darrell
arXiv preprint arXiv:1605.09782, 2016
5302016
Generative visual manipulation on the natural image manifold
JY Zhu, P Krähenbühl, E Shechtman, AA Efros
European Conference on Computer Vision, 597-613, 2016
4402016
Geodesic object proposals
P Krähenbühl, V Koltun
European conference on computer vision, 725-739, 2014
3302014
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
2572015
A system for retargeting of streaming video
P Krähenbühl, M Lang, A Hornung, M Gross
ACM Transactions on Graphics (TOG) 28 (5), 126, 2009
2392009
Parameter learning and convergent inference for dense random fields
P Krähenbühl, V Koltun
International Conference on Machine Learning, 513-521, 2013
1572013
SWPS3–fast multi-threaded vectorized Smith-Waterman for IBM Cell/BE and× 86/SSE2
A Szalkowski, C Ledergerber, P Krähenbühl, C Dessimoz
BMC research notes 1 (1), 107, 2008
1322008
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
1292016
Sampling matters in deep embedding learning
CY Wu, R Manmatha, AJ Smola, P Krähenbühl
ICCV 2017, 2017
1192017
Data-dependent initializations of convolutional neural networks
P Krähenbühl, C Doersch, J Donahue, T Darrell
arXiv preprint arXiv:1511.06856, 2015
1152015
Gesture controllers
S Levine, P Krähenbühl, S Thrun, V Koltun
ACM Transactions on Graphics (TOG) 29 (4), 124, 2010
932010
Learning data-driven reflectance priors for intrinsic image decomposition
T Zhou, P Krahenbuhl, AA Efros
Proceedings of the IEEE International Conference on Computer Vision, 3469-3477, 2015
742015
Learning to Propose Objects
P Krähenbühl, V Koltun
Computer Vision and Pattern Recognition (CVPR), 2015
722015
Efficient nonlocal regularization for optical flow
P Krähenbühl, V Koltun
European Conference on Computer Vision, 356-369, 2012
502012
Learning a discriminative model for the perception of realism in composite images
JY Zhu, P Krahenbuhl, E Shechtman, AA Efros
Proceedings of the IEEE International Conference on Computer Vision, 3943-3951, 2015
432015
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
412018
Visual saliency estimation for images and video
F Perazzi, A Hornung, P Krähenbühl, Y Pritch
US Patent 9,025,880, 2015
362015
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