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Evan Shelhamer
Evan Shelhamer
UBC / Vector Institute / Google DeepMind
Verificeret mail på cs.ubc.ca - Startside
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Fully convolutional networks for semantic segmentation
J Long, E Shelhamer, T Darrell
Proceedings of the IEEE conference on computer vision and pattern …, 2015
473462015
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
186522014
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
87842016
cudnn: Efficient primitives for deep learning
S Chetlur, C Woolley, P Vandermersch, J Cohen, J Tran, B Catanzaro, ...
arXiv preprint arXiv:1410.0759, 2014
24652014
Deep layer aggregation
F Yu, D Wang, E Shelhamer, T Darrell
CVPR, 2018
17642018
Tent: Fully Test-Time Adaptation by Entropy Minimization
D Wang, E Shelhamer, S Liu, B Olshausen, T Darrell
ICLR, 2021
12082021
Perceiver io: A general architecture for structured inputs & outputs
A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ...
ICLR, 2021
6402021
Fully convolutional multi-class multiple instance learning
D Pathak, E Shelhamer, J Long, T Darrell
arXiv preprint arXiv:1412.7144, 2014
4012014
Zero-shot visual imitation
D Pathak, P Mahmoudieh, G Luo, P Agrawal, D Chen, Y Shentu, ...
ICLR, 2018
3282018
Infinite Mixture Prototypes for Few-Shot Learning
KR Allen, E Shelhamer, H Shin, JB Tenenbaum
ICML, 232--241, 2019
3142019
Clockwork convnets for video semantic segmentation
E Shelhamer, K Rakelly, J Hoffman, T Darrell
European Conference on Computer Vision Workshops, 852-868, 2016
2712016
Conditional networks for few-shot semantic segmentation
K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine
2462018
Loss is its own reward: Self-supervision for reinforcement learning
E Shelhamer, P Mahmoudieh, M Argus, T Darrell
arXiv preprint arXiv:1612.07307, 2016
2222016
Few-shot segmentation propagation with guided networks
K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine
arXiv preprint arXiv:1806.07373, 2018
1452018
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
1092023
Object discovery and representation networks
OJ Hénaff, S Koppula, E Shelhamer, D Zoran, A Jaegle, A Zisserman, ...
ECCV, 2022
992022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
F Croce, S Gowal, T Brunner, E Shelhamer, M Hein, T Cemgil
ICML, 2022
812022
Fine-grained pose prediction, normalization, and recognition
N Zhang, E Shelhamer, Y Gao, T Darrell
arXiv preprint arXiv:1511.07063, 2015
772015
Scene intrinsics and depth from a single image
E Shelhamer, JT Barron, T Darrell
Proceedings of the IEEE International Conference on Computer Vision …, 2015
622015
On-target Adaptation
D Wang, S Liu, S Ebrahimi, E Shelhamer, T Darrell
arXiv preprint arXiv:2109.01087, 2021
37*2021
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