Seguir
David Acuna
David Acuna
Dirección de correo verificada de cs.toronto.edu - Página principal
Título
Citado por
Citado por
Año
Training deep networks with synthetic data: Bridging the reality gap by domain randomization
J Tremblay, A Prakash, D Acuna, M Brophy, V Jampani, C Anil, T To, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2018
9092018
Gated-scnn: Gated shape cnns for semantic segmentation
T Takikawa, D Acuna, V Jampani, S Fidler
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
7102019
Efficient interactive annotation of segmentation datasets with polygon-rnn++
D Acuna, H Ling, A Kar, S Fidler
Proceedings of the IEEE conference on Computer Vision and Pattern …, 2018
4482018
Meta-sim: Learning to generate synthetic datasets
A Kar, A Prakash, MY Liu, E Cameracci, J Yuan, M Rusiniak, D Acuna, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
2522019
Devil is in the edges: Learning semantic boundaries from noisy annotations
D Acuna, A Kar, S Fidler
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
1802019
Structured domain randomization: Bridging the reality gap by context-aware synthetic data
A Prakash, S Boochoon, M Brophy, D Acuna, E Cameracci, G State, ...
2019 International Conference on Robotics and Automation (ICRA), 7249-7255, 2019
1642019
Gavriel State, Omer Shapira, and Stan Birchfield. Structured domain randomization: Bridging the reality gap by context-aware synthetic data
A Prakash, S Boochoon, M Brophy, D Acuna, E Cameracci
2019 International Conference on Robotics and Automation (ICRA), 7249-7255, 2019
1442019
Object instance annotation with deep extreme level set evolution
Z Wang, D Acuna, H Ling, A Kar, S Fidler
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
812019
Neural turtle graphics for modeling city road layouts
H Chu, D Li, D Acuna, A Kar, M Shugrina, X Wei, MY Liu, A Torralba, ...
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
742019
f-domain adversarial learning: Theory and algorithms
D Acuna, G Zhang, MT Law, S Fidler
International Conference on Machine Learning, 66-75, 2021
612021
Neural data server: A large-scale search engine for transfer learning data
X Yan, D Acuna, S Fidler
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
452020
Variational amodal object completion
H Ling, D Acuna, K Kreis, SW Kim, S Fidler
Advances in Neural Information Processing Systems 33, 16246-16257, 2020
342020
Towards real-time detection and tracking of basketball players using deep neural networks
D Acuna
Proceedings of the 31st Conference on Neural Information Processing Systems …, 2017
222017
Generation of synthetic images for training a neural network model
J Tremblay, A Prakash, MA Brophy, V Jampani, C Anil, ST Birchfield, ...
US Patent 10,867,214, 2020
212020
Neural light field estimation for street scenes with differentiable virtual object insertion
Z Wang, W Chen, D Acuna, J Kautz, S Fidler
European Conference on Computer Vision, 380-397, 2022
202022
Towards optimal strategies for training self-driving perception models in simulation
D Acuna, J Philion, S Fidler
Advances in Neural Information Processing Systems 34, 1686-1699, 2021
172021
How much more data do i need? estimating requirements for downstream tasks
R Mahmood, J Lucas, D Acuna, D Li, J Philion, JM Alvarez, Z Yu, S Fidler, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
162022
Federated learning with heterogeneous architectures using graph hypernetworks
O Litany, H Maron, D Acuna, J Kautz, G Chechik, S Fidler
arXiv preprint arXiv:2201.08459, 2022
152022
Systems and methods for polygon object annotation and a method of training and object annotation system
S Fidler, A Kar, H Ling, J Gao, W Chen, DJA Marrero
US Patent 10,643,130, 2020
142020
Domain adversarial training: A game perspective
D Acuna, MT Law, G Zhang, S Fidler
arXiv preprint arXiv:2202.05352, 2022
122022
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20