| 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 | 5213 | 2016 |
| Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials P Krähenbühl, V Koltun NIPS, 2011 | 3578 | 2011 |
| Objects as points X Zhou, D Wang, P Krähenbühl arXiv preprint arXiv:1904.07850, 2019 | 2785 | 2019 |
| Adversarial feature learning J Donahue, P Krähenbühl, T Darrell arXiv preprint arXiv:1605.09782, 2016 | 2023 | 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 | 1979 | 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 | 1375 | 2016 |
| Sampling matters in deep embedding learning CY Wu, R Manmatha, AJ Smola, P Krähenbühl ICCV 2017, 2017 | 899 | 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 | 713 | 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 | 690 | 2015 |
| Tracking Objects as Points X Zhou, V Koltun, P Krähenbühl ECCV, 2020 | 686 | 2020 |
| Geodesic object proposals P Krähenbühl, V Koltun Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 447 | 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 | 435 | 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 | 373 | 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 | 267 | 2019 |
| Parameter learning and convergent inference for dense random fields P Krähenbühl, V Koltun International Conference on Machine Learning, 513-521, 2013 | 250 | 2013 |
| Data-dependent initializations of convolutional neural networks P Krähenbühl, C Doersch, J Donahue, T Darrell arXiv preprint arXiv:1511.06856, 2015 | 231 | 2015 |
Vladlen KoltunDistinguished Scientist, Apple在 apple.com 的电子邮件经过验证
