| Adapting visual category models to new domains K Saenko, B Kulis, M Fritz, T Darrell European conference on computer vision, 213-226, 2010 | 2011 | 2010 |
| A multi-world approach to question answering about real-world scenes based on uncertain input M Malinowski, M Fritz Advances in neural information processing systems 27, 1682-1690, 2014 | 630 | 2014 |
| Ask your neurons: A neural-based approach to answering questions about images M Malinowski, M Rohrbach, M Fritz Proceedings of the IEEE international conference on computer vision, 1-9, 2015 | 606 | 2015 |
| Discovery of activity patterns using topic models T Huynh, M Fritz, B Schiele Proceedings of the 10th international conference on Ubiquitous computing, 10-19, 2008 | 552 | 2008 |
| Appearance-based gaze estimation in the wild X Zhang, Y Sugano, M Fritz, A Bulling Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 522 | 2015 |
| A category-level 3d object dataset: Putting the kinect to work A Janoch, S Karayev, Y Jia, JT Barron, M Fritz, K Saenko, T Darrell Consumer depth cameras for computer vision, 141-165, 2013 | 478 | 2013 |
| On the significance of real-world conditions for material classification E Hayman, B Caputo, M Fritz, JO Eklundh European conference on computer vision, 253-266, 2004 | 410 | 2004 |
| Disentangled person image generation L Ma, Q Sun, S Georgoulis, L Van Gool, B Schiele, M Fritz Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 327 | 2018 |
| The 2005 pascal visual object classes challenge M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ... Machine Learning Challenges Workshop, 117-176, 2005 | 314 | 2005 |
| Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models A Salem, Y Zhang, M Humbert, P Berrang, M Fritz, M Backes arXiv preprint arXiv:1806.01246, 2018 | 249 | 2018 |
| Mpiigaze: Real-world dataset and deep appearance-based gaze estimation X Zhang, Y Sugano, M Fritz, A Bulling IEEE transactions on pattern analysis and machine intelligence 41 (1), 162-175, 2017 | 210 | 2017 |
| Vconv-dae: Deep volumetric shape learning without object labels A Sharma, O Grau, M Fritz European Conference on Computer Vision, 236-250, 2016 | 209 | 2016 |
| A geometric approach to robotic laundry folding S Miller, J Van Den Berg, M Fritz, T Darrell, K Goldberg, P Abbeel The International Journal of Robotics Research 31 (2), 249-267, 2012 | 201 | 2012 |
| It's written all over your face: Full-face appearance-based gaze estimation X Zhang, Y Sugano, M Fritz, A Bulling Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 196 | 2017 |
| Advanced steel microstructural classification by deep learning methods SM Azimi, D Britz, M Engstler, M Fritz, F Mücklich Scientific reports 8 (1), 1-14, 2018 | 185 | 2018 |
| Towards reverse-engineering black-box neural networks SJ Oh, B Schiele, M Fritz Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 121-144, 2019 | 184 | 2019 |
| Speaking the same language: Matching machine to human captions by adversarial training R Shetty, M Rohrbach, L Anne Hendricks, M Fritz, B Schiele Proceedings of the IEEE International Conference on Computer Vision, 4135-4144, 2017 | 182 | 2017 |
| Integrating representative and discriminant models for object category detection M Fritz, B Leibe, B Caputo, B Schiele Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 2 …, 2005 | 173 | 2005 |
| Exploiting saliency for object segmentation from image level labels SJ Oh, R Benenson, A Khoreva, Z Akata, M Fritz, B Schiele 2017 IEEE conference on computer vision and pattern recognition (CVPR), 5038 …, 2017 | 152* | 2017 |
| Knockoff nets: Stealing functionality of black-box models T Orekondy, B Schiele, M Fritz Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 146 | 2019 |
