| A broad study of pre-training for domain generalization and adaptation D Kim, K Wang, S Sclaroff, K Saenko European Conference on Computer Vision, 621-638, 2022 | 61 | 2022 |
| A broader study of cross-domain few-shot learning Y Guo, NC Codella, L Karlinsky, JV Codella, JR Smith, K Saenko, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 321 | 2020 |
| 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 | 566 | 2013 |
| A combined pose, object, and feature model for action understanding B Packer, K Saenko, D Koller 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1378-1385, 2012 | 98 | 2012 |
| A dataset for interactive vision-language navigation with unknown command feasibility A Burns, D Arsan, S Agrawal, R Kumar, K Saenko, BA Plummer European Conference on Computer Vision, 312-328, 2022 | 40 | 2022 |
| A multi-scale multiple instance video description network H Xu, S Venugopalan, V Ramanishka, M Rohrbach, K Saenko arXiv preprint arXiv:1505.05914, 2015 | 70 | 2015 |
| A Segment-Based Audio-Visual Speech Recognition System TJ Hazen, K Saenko, JR Glass | | |
| A segment-based audio-visual speech recognizer: Data collection, development, and initial experiments TJ Hazen, K Saenko, CH La, JR Glass Proceedings of the 6th international conference on Multimodal interfaces …, 2004 | 166 | 2004 |
| A suite of generative tasks for multi-level multimodal webpage understanding A Burns, K Srinivasan, J Ainslie, G Brown, BA Plummer, K Saenko, J Ni, ... arXiv preprint arXiv:2305.03668, 2023 | 4 | 2023 |
| A two-stream variational adversarial network for video generation X Sun, H Xu, K Saenko arXiv preprint arXiv:1812.01037 1, 12, 2018 | 15 | 2018 |
| A unified framework for domain adaptive pose estimation D Kim, K Wang, K Saenko, M Betke, S Sclaroff European Conference on Computer Vision, 603-620, 2022 | 23 | 2022 |
| Active domain adaptation via clustering uncertainty-weighted embeddings V Prabhu, A Chandrasekaran, K Saenko, J Hoffman Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 125 | 2021 |
| Adafuse: Adaptive temporal fusion network for efficient action recognition Y Meng, R Panda, CC Lin, P Sattigeri, L Karlinsky, K Saenko, A Oliva, ... arXiv preprint arXiv:2102.05775, 2021 | 65 | 2021 |
| Adamml: Adaptive multi-modal learning for efficient video recognition R Panda, CFR Chen, Q Fan, X Sun, K Saenko, A Oliva, R Feris Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 50 | 2021 |
| AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition (Supplementary Material) R Panda, CFR Chen, Q Fan, X Sun, K Saenko, A Oliva, R Feris | | |
| Adapting control policies from simulation to reality using a pairwise loss U Viereck, X Peng, K Saenko, R Platt Proceedings of the 2018 International Symposium on Experimental Robotics …, 2020 | 2 | 2020 |
| Adapting deep visuomotor representations with weak pairwise constraints E Tzeng, C Devin, J Hoffman, C Finn, P Abbeel, S Levine, K Saenko, ... Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop …, 2020 | 160 | 2020 |
| Adapting visual category models to new domains K Saenko, B Kulis, M Fritz, T Darrell Computer Vision–ECCV 2010: 11th European Conference on Computer Vision …, 2010 | 3329 | 2010 |
| Adashare: Learning what to share for efficient deep multi-task learning X Sun, R Panda, R Feris, K Saenko Advances in Neural Information Processing Systems 33, 8728-8740, 2020 | 239 | 2020 |
| AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (Supplementary Material) X Sun, R Panda, R Feris, K Saenko | | |
Trevor DarrellProfessor of Computer Science, U.C. Berkeley在 eecs.berkeley.edu 的电子邮件经过验证
