| Long-term recurrent convolutional networks for visual recognition and description J Donahue, L Anne Hendricks, S Guadarrama, M Rohrbach, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 6021 | 2015 |
| Adversarial discriminative domain adaptation E Tzeng, J Hoffman, K Saenko, T Darrell Computer Vision and Pattern Recognition (CVPR), 2017 | 3220 | 2017 |
| Adapting visual category models to new domains K Saenko, B Kulis, M Fritz, T Darrell European conference on computer vision, 213-226, 2010 | 2253 | 2010 |
| Cycada: Cycle-consistent adversarial domain adaptation J Hoffman, E Tzeng, T Park, JY Zhu, P Isola, K Saenko, AA Efros, T Darrell International Conference on Machine Learning 2018, 2018 | 1935 | 2018 |
| Deep domain confusion: Maximizing for domain invariance E Tzeng, J Hoffman, N Zhang, K Saenko, T Darrell arXiv preprint arXiv:1412.3474, 2014 | 1821 | 2014 |
| Deep coral: Correlation alignment for deep domain adaptation B Sun, K Saenko European conference on computer vision, 443-450, 2016 | 1525 | 2016 |
| Sequence to sequence-video to text S Venugopalan, M Rohrbach, J Donahue, R Mooney, T Darrell, K Saenko Proceedings of the IEEE international conference on computer vision, 4534-4542, 2015 | 1346 | 2015 |
| Return of Frustratingly Easy Domain Adaptation B Sun, J Feng, K Saenko AAAI, 2016 | 1241 | 2016 |
| Simultaneous deep transfer across domains and tasks E Tzeng, J Hoffman, T Darrell, K Saenko Proceedings of the IEEE international conference on computer vision, 4068-4076, 2015 | 1207 | 2015 |
| Translating videos to natural language using deep recurrent neural networks S Venugopalan, H Xu, J Donahue, M Rohrbach, R Mooney, K Saenko NAACL HLT 2015, 2014 | 958 | 2014 |
| What you saw is not what you get: Domain adaptation using asymmetric kernel transforms B Kulis, K Saenko, T Darrell CVPR 2011, 1785-1792, 2011 | 792 | 2011 |
| Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering H Xu, K Saenko European Conference on Computer Vision (ECCV), 2016 | 771 | 2016 |
| R-C3D: Region convolutional 3d network for temporal activity detection H Xu, A Das, K Saenko International Conference on Computer Vision (ICCV), 2017 | 601 | 2017 |
| Moment matching for multi-source domain adaptation X Peng, Q Bai, X Xia, Z Huang, K Saenko, B Wang Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 547 | 2019 |
| 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 | 504 | 2013 |
| Learning to reason: End-to-end module networks for visual question answering R Hu, J Andreas, M Rohrbach, T Darrell, K Saenko International Conference on Computer Vision (ICCV), 2017 | 492 | 2017 |
| Youtube2text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition S Guadarrama, N Krishnamoorthy, G Malkarnenkar, S Venugopalan, ... Computer Vision (ICCV), 2013 IEEE International Conference on, 2712-2719, 2013 | 481 | 2013 |
| Natural language object retrieval R Hu, H Xu, M Rohrbach, J Feng, K Saenko, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 477 | 2016 |
| Rise: Randomized input sampling for explanation of black-box models V Petsiuk, A Das, K Saenko arXiv preprint arXiv:1806.07421, 2018 | 420 | 2018 |
| Learning deep object detectors from 3d models X Peng, B Sun, K Ali, K Saenko Proceedings of the IEEE international conference on computer vision, 1278-1286, 2015 | 353 | 2015 |
Trevor DarrellProfessor of Computer Science, U.C. Berkeley確認したメール アドレス: eecs.berkeley.edu
