Evan Shelhamer
Evan Shelhamer
Adobe | MIT | UC Berkeley
Verified email at cs.berkeley.edu - Homepage
Cited by
Cited by
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
Fully convolutional networks for semantic segmentation
J Long, E Shelhamer, T Darrell
Proceedings of the IEEE conference on computer vision and pattern …, 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
cudnn: Efficient primitives for deep learning
S Chetlur, C Woolley, P Vandermersch, J Cohen, J Tran, B Catanzaro, ...
arXiv preprint arXiv:1410.0759, 2014
Fully convolutional multi-class multiple instance learning
D Pathak, E Shelhamer, J Long, T Darrell
arXiv preprint arXiv:1412.7144, 2014
Deep layer aggregation
F Yu, D Wang, E Shelhamer, T Darrell
arXiv preprint arXiv:1707.06484, 2017
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
Clockwork convnets for video semantic segmentation
E Shelhamer, K Rakelly, J Hoffman, T Darrell
European Conference on Computer Vision Workshops, 852-868, 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
Fine-grained pose prediction, normalization, and recognition
N Zhang, E Shelhamer, Y Gao, T Darrell
arXiv preprint arXiv:1511.07063, 2015
Conditional networks for few-shot semantic segmentation
K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine
Few-shot segmentation propagation with guided networks
K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine
arXiv preprint arXiv:1806.07373, 2018
Infinite Mixture Prototypes for Few-Shot Learning
KR Allen, E Shelhamer, H Shin, JB Tenenbaum
ICML, 232--241, 2019
Scene intrinsics and depth from a single image
E Shelhamer, JT Barron, T Darrell
Proceedings of the IEEE International Conference on Computer Vision …, 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
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
Communal cuts: sharing cuts across images
E Shelhamer, S Jegelka, T Darrell
NeurIPS Workshop on Discrete Optimization in Machine Learning, 2014
Dynamic scale inference by entropy minimization
D Wang, E Shelhamer, B Olshausen, T Darrell
arXiv preprint arXiv:1908.03182, 2019
Fully Test-time Adaptation by Entropy Minimization
D Wang, E Shelhamer, S Liu, B Olshausen, T Darrell
arXiv preprint arXiv:2006.10726, 2020
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