David Acuna
David Acuna
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
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
2312018
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
1282018
Gated-scnn: Gated shape cnns for semantic segmentation
T Takikawa, D Acuna, V Jampani, S Fidler
Proceedings of the IEEE International Conference on Computer Vision, 5229-5238, 2019
622019
Devil is in the edges: Learning semantic boundaries from noisy annotations
D Acuna, A Kar, S Fidler
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
422019
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 International Conference on Computer Vision, 4551-4560, 2019
382019
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
352019
Object instance annotation with deep extreme level set evolution
Z Wang, D Acuna, H Ling, A Kar, S Fidler
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
232019
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 International Conference on Computer Vision, 4522-4530, 2019
142019
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
arXiv preprint arXiv:1810.10093 1 (2), 7, 2018
132018
Towards real-time detection and tracking of basketball players using deep neural networks
D Acuna
31st Conference on Neural Information Processing Systems (NIPS 2017), 2017
52017
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
22020
Generation of Synthetic Images For Training a Neural Network Model
J Tremblay, A Prakash, MA Brophy, V Jampani, C Anil, ST Birchfield, ...
US Patent App. 16/256,820, 2019
12019
Generating Class-conditional Images with Gradient-based Inference
B Xu, D Acuņa, D Duvenaud
NIPS Workshop in Constructive Machine Learning, 2016
12016
Unsupervised modeling of the movement of basketball players using a deep generative model
D Acuna
1
Learning to generate synthetic datasets for traning neural networks
A Kar, A Prakash, MY Liu, DJA Marrero, AT Barriuso, S Fidler
US Patent App. 16/685,795, 2020
2020
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
2020
WHAT DATA IS USEFUL FOR MY DATA: TRANSFER LEARNING WITH A MIXTURE OF SELF-SUPERVISED EXPERTS
X Yan, D Acuna, S Fidler
2019
Meta-Sim: Learning to Generate Synthetic Datasets Download PDF
A Kar, A Prakash, MY Liu, E Cameracci, J Yuan, M Rusiniak, D Acuna, ...
Supplementary Material: Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data
X Yan, D Acuna, S Fidler
Supplementary Material: Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN+
D Acuna, H Ling, A Kar, S Fidler
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