| Fully Convolutional Networks for Semantic Segmentation E Shelhamer, J Long, T Darrell IEEE Transactions on pattern analysis and machine intelligence 39 (4), 640-651, 2017 | 21152* | 2017 |
| Fully convolutional networks for semantic segmentation J Long, E Shelhamer, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 21001 | 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 | 15018 | 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 | 1294 | 2014 |
| Deep layer aggregation F Yu, D Wang, E Shelhamer, T Darrell arXiv preprint arXiv:1707.06484, 2017 | 336 | 2017 |
| Fully convolutional multi-class multiple instance learning D Pathak, E Shelhamer, J Long, T Darrell arXiv preprint arXiv:1412.7144, 2014 | 271 | 2014 |
| Zero-shot visual imitation D Pathak, P Mahmoudieh, G Luo, P Agrawal, D Chen, Y Shentu, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 144 | 2018 |
| Clockwork convnets for video semantic segmentation E Shelhamer, K Rakelly, J Hoffman, T Darrell European Conference on Computer Vision Workshops, 852-868, 2016 | 128 | 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 | 88 | 2016 |
| Fine-grained pose prediction, normalization, and recognition N Zhang, E Shelhamer, Y Gao, T Darrell arXiv preprint arXiv:1511.07063, 2015 | 64 | 2015 |
| Conditional networks for few-shot semantic segmentation K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine | 51 | 2018 |
| Infinite Mixture Prototypes for Few-Shot Learning KR Allen, E Shelhamer, H Shin, JB Tenenbaum ICML, 232--241, 2019 | 49 | 2019 |
| Few-shot segmentation propagation with guided networks K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine arXiv preprint arXiv:1806.07373, 2018 | 44 | 2018 |
| Scene intrinsics and depth from a single image E Shelhamer, JT Barron, T Darrell Proceedings of the IEEE International Conference on Computer Vision …, 2015 | 33 | 2015 |
| 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 | 13 | 2019 |
| Fully test-time adaptation by entropy minimization D Wang, E Shelhamer, S Liu, B Olshausen, T Darrell arXiv preprint arXiv:2006.10726, 2020 | 4 | 2020 |
| Diy deep learning for vision: a hands-on tutorial with caffe E Shelhamer, J Donahue, J Long, Y Jia, R Girshick Proceedings of the 13th European Conference on Computer Vision Tutorials, 2014 | 4 | 2014 |
| Communal cuts: sharing cuts across images E Shelhamer, S Jegelka, T Darrell NeurIPS Workshop on Discrete Optimization in Machine Learning, 2014 | 3 | 2014 |
| Transferable recognition-aware image processing Z Liu, T Zhou, HJ Wang, Z Shen, B Kang, E Shelhamer, T Darrell arXiv preprint arXiv:1910.09185, 2019 | 2 | 2019 |
| Dynamic scale inference by entropy minimization D Wang, E Shelhamer, B Olshausen, T Darrell arXiv preprint arXiv:1908.03182, 2019 | 2 | 2019 |
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
