Obserwuj
Philipp Krähenbühl
Philipp Krähenbühl
UT Austin
Zweryfikowany adres z cs.utexas.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
69692016
Objects as points
X Zhou, D Wang, P Krähenbühl
arXiv preprint arXiv:1904.07850, 2019
47122019
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
P Krähenbühl, V Koltun
NIPS, 2011
42212011
Adversarial feature learning
J Donahue, P Krähenbühl, T Darrell
arXiv preprint arXiv:1605.09782, 2016
26372016
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
24812012
Center-based 3d object detection and tracking
T Yin, X Zhou, P Krähenbühl
CVPR, 2021
19952021
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
16932016
Tracking Objects as Points
X Zhou, V Koltun, P Krähenbühl
ECCV, 2020
14032020
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
12242019
Sampling matters in deep embedding learning
CY Wu, R Manmatha, AJ Smola, P Krähenbühl
ICCV 2017, 2017
11472017
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
7832015
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
6502022
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
6042019
Learning by cheating
D Chen, B Zhou, V Koltun, P Krähenbühl
Conference on Robot Learning, 66-75, 2019
5562019
Geodesic object proposals
P Krähenbühl, V Koltun
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014
4762014
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
4492016
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
4212018
Video compression through image interpolation
CY Wu, N Singhal, P Krahenbuhl
Proceedings of the European conference on computer vision (ECCV), 416-431, 2018
4092018
Probabilistic two-stage detection
X Zhou, V Koltun, P Krähenbühl
arXiv preprint arXiv:2103.07461, 2021
3172021
A system for retargeting of streaming video
P Krähenbühl, M Lang, A Hornung, M Gross
ACM SIGGRAPH Asia 2009 papers, 1-10, 2009
3082009
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