Evan Shelhamer
Evan Shelhamer
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Cited by
Cited by
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
Deep layer aggregation
F Yu, D Wang, E Shelhamer, T Darrell
arXiv preprint arXiv:1707.06484, 2017
Fully convolutional multi-class multiple instance learning
D Pathak, E Shelhamer, J Long, T Darrell
arXiv preprint arXiv:1412.7144, 2014
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
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
Tent: Fully Test-Time Adaptation by Entropy Minimization
D Wang, E Shelhamer, S Liu, B Olshausen, T Darrell
International Conference on Learning Representations 4, 6, 2021
Infinite Mixture Prototypes for Few-Shot Learning
KR Allen, E Shelhamer, H Shin, JB Tenenbaum
ICML, 232--241, 2019
Loss is its own reward: Self-supervision for reinforcement learning
E Shelhamer, P Mahmoudieh, M Argus, T Darrell
arXiv preprint arXiv:1612.07307, 2016
Conditional networks for few-shot semantic segmentation
K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine
Perceiver io: A general architecture for structured inputs & outputs
A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ...
arXiv preprint arXiv:2107.14795, 2021
Few-shot segmentation propagation with guided networks
K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine
arXiv preprint arXiv:1806.07373, 2018
Fine-grained pose prediction, normalization, and recognition
N Zhang, E Shelhamer, Y Gao, T Darrell
arXiv preprint arXiv:1511.07063, 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
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
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
D Wang, A Ju, E Shelhamer, D Wagner, T Darrell
arXiv preprint arXiv:2105.08714, 2021
Exploring Simple and Transferable Recognition-Aware Image Processing
Z Liu, H Wang, T Zhou, Z Shen, B Kang, E Shelhamer, T Darrell
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
F Croce, S Gowal, T Brunner, E Shelhamer, M Hein, T Cemgil
arXiv preprint arXiv:2202.13711, 2022
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