| Learning to adapt structured output space for semantic segmentation YH Tsai, WC Hung, S Schulter, K Sohn, MH Yang, M Chandraker Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1761 | 2018 |
| Fast and accurate image upscaling with super-resolution forests S Schulter, C Leistner, H Bischof Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 758 | 2015 |
| Domain adaptation for structured output via discriminative patch representations YH Tsai, K Sohn, S Schulter, M Chandraker Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 384 | 2019 |
| Deep network flow for multi-object tracking S Schulter, P Vernaza, W Choi, M Chandraker Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 254 | 2017 |
| You should use regression to detect cells P Kainz, M Urschler, S Schulter, P Wohlhart, V Lepetit Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th …, 2015 | 148 | 2015 |
| Learning to simulate N Ruiz, S Schulter, M Chandraker arXiv preprint arXiv:1810.02513, 2018 | 142 | 2018 |
| Conditioned regression models for non-blind single image super-resolution G Riegler, S Schulter, M Ruther, H Bischof Proceedings of the IEEE International Conference on Computer Vision, 522-530, 2015 | 114 | 2015 |
| Shuffle and attend: Video domain adaptation J Choi, G Sharma, S Schulter, JB Huang Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 110 | 2020 |
| Domain adaptive semantic segmentation using weak labels S Paul, YH Tsai, S Schulter, AK Roy-Chowdhury, M Chandraker Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 95 | 2020 |
| Learning to look around objects for top-view representations of outdoor scenes S Schulter, M Zhai, N Jacobs, M Chandraker Proceedings of the European Conference on Computer Vision (ECCV), 787-802, 2018 | 93 | 2018 |
| Exploiting unlabeled data with vision and language models for object detection S Zhao, Z Zhang, S Schulter, L Zhao, BG Vijay Kumar, A Stathopoulos, ... European conference on computer vision, 159-175, 2022 | 87 | 2022 |
| Alternating Decision Forests S Schulter, P Wohlhart, C Leistner, A Saffari, PM Roth, H Bischof IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, 2013 | 83 | 2013 |
| Alternating regression forests for object detection and pose estimation S Schulter, C Leistner, P Wohlhart, PM Roth, H Bischof Proceedings of the IEEE International Conference on Computer Vision, 417-424, 2013 | 66 | 2013 |
| Object detection with a unified label space from multiple datasets X Zhao, S Schulter, G Sharma, YH Tsai, M Chandraker, Y Wu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 64 | 2020 |
| Interactive 3D segmentation of rock-art by enhanced depth maps and gradient preserving regularization M Zeppelzauer, G Poier, M Seidl, C Reinbacher, S Schulter, ... Journal on Computing and Cultural Heritage (JOCCH) 9 (4), 1-30, 2016 | 56 | 2016 |
| Mm-tta: multi-modal test-time adaptation for 3d semantic segmentation I Shin, YH Tsai, B Zhuang, S Schulter, B Liu, S Garg, IS Kweon, KJ Yoon Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 55 | 2022 |
| Accurate Object Detection with Joint Classification-Regression Random Forests S Schulter, C Leistner, P Wohlhart, PM Roth, H Bischof IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, 2014 | 55 | 2014 |
| A parametric top-view representation of complex road scenes Z Wang, B Liu, S Schulter, M Chandraker Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 48 | 2019 |
| On-line Hough Forests S Schulter, C Leistner, PM Roth, L Van Gool, H Bischof British Machine Vision Conference, 2011 | 45 | 2011 |
| Improving classifiers with unlabeled weakly-related videos C Leistner, M Godec, S Schulter, A Saffari, M Werlberger, H Bischof CVPR 2011, 2753-2760, 2011 | 44 | 2011 |
Manmohan ChandrakerProfessor, University of California, San DiegoBestätigte E-Mail-Adresse bei ucsd.edu
