| Fully convolutional networks for semantic segmentation J Long, E Shelhamer, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 45126 | 2015 |
| Caffe: Convolutional architecture for fast feature embedding Y Jia, E Shelhamer, J Donahue, S Karayev, J Long, R Girshick, ... Proceedings of the 22nd ACM international conference on Multimedia, 675-678, 2014 | 17930 | 2014 |
| cudnn: Efficient primitives for deep learning S Chetlur, C Woolley, P Vandermersch, J Cohen, J Tran, B Catanzaro, ... arXiv preprint arXiv:1410.0759, 2014 | 2152 | 2014 |
| Deep layer aggregation F Yu, D Wang, E Shelhamer, T Darrell CVPR, 2018 | 1369 | 2018 |
| Tent: Fully Test-Time Adaptation by Entropy Minimization D Wang, E Shelhamer, S Liu, B Olshausen, T Darrell ICLR, 2021 | 589 | 2021 |
| Fully convolutional multi-class multiple instance learning D Pathak, E Shelhamer, J Long, T Darrell arXiv preprint arXiv:1412.7144, 2014 | 367 | 2014 |
| Perceiver io: A general architecture for structured inputs & outputs A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ... ICLR, 2021 | 360 | 2021 |
| Zero-shot visual imitation D Pathak, P Mahmoudieh, G Luo, P Agrawal, D Chen, Y Shentu, ... ICLR, 2018 | 283 | 2018 |
| Infinite Mixture Prototypes for Few-Shot Learning KR Allen, E Shelhamer, H Shin, JB Tenenbaum ICML, 232--241, 2019 | 255 | 2019 |
| Clockwork convnets for video semantic segmentation E Shelhamer, K Rakelly, J Hoffman, T Darrell European Conference on Computer Vision Workshops, 852-868, 2016 | 232 | 2016 |
| Conditional networks for few-shot semantic segmentation K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine | 204 | 2018 |
| Fully Convolutional Networks for Semantic Segmentation E Shelhamer, J Long, T Darrell IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4), 640-651, 2016 | 193 | 2016 |
| Loss is its own reward: Self-supervision for reinforcement learning E Shelhamer, P Mahmoudieh, M Argus, T Darrell arXiv preprint arXiv:1612.07307, 2016 | 182 | 2016 |
| Few-shot segmentation propagation with guided networks K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine arXiv preprint arXiv:1806.07373, 2018 | 116 | 2018 |
| Fine-grained pose prediction, normalization, and recognition N Zhang, E Shelhamer, Y Gao, T Darrell arXiv preprint arXiv:1511.07063, 2015 | 76 | 2015 |
| Scene intrinsics and depth from a single image E Shelhamer, JT Barron, T Darrell Proceedings of the IEEE International Conference on Computer Vision …, 2015 | 59 | 2015 |
| Object discovery and representation networks OJ Hénaff, S Koppula, E Shelhamer, D Zoran, A Jaegle, A Zisserman, ... ECCV, 2022 | 47 | 2022 |
| Evaluating the Adversarial Robustness of Adaptive Test-time Defenses F Croce, S Gowal, T Brunner, E Shelhamer, M Hein, T Cemgil ICML, 2022 | 41 | 2022 |
| Blurring the line between structure and learning to optimize and adapt receptive fields E Shelhamer, D Wang, T Darrell arXiv preprint arXiv:1904.11487, 2019 | 32 | 2019 |
| Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption J Gao, J Zhang, X Liu, T Darrell, E Shelhamer, D Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 30* | 2023 |
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
