David Acuna

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 7 papers; trusts 912 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 217 citations into it; #4,090 of 21,575 by trust here, #3,875 by citations
• Artificial Intelligence — 70 citations into it; #34,019 of 35,345 by trust here, #14,438 by citations
• Evaluation & Benchmarks — 38 citations into it; #1,682 of 3,147 by trust here, #1,740 by citations
• Model Architectures — 29 citations into it
• Aerospace Engineering — 27 citations into it
• Reinforcement Learning — 12 citations into it
• Computational Mechanics — 11 citations into it
• Optimization & Training — 11 citations into it

Whom David Acuna trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Ross B. Girshick
3. Liang-Chieh Chen
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Shaoqing Ren
7. Yann LeCun
8. Diederik P. Kingma
9. Yoshua Bengio
10. Christian Szegedy

Who trusts David Acuna overall

1. J. Liew
2. Huan Ling
3. Yuhui Yuan
4. Justin Liang
5. Scott D. Cohen
6. Brian L. Price
7. Shiyin Zhang
8. Peng Zhou
9. Sanja Fidler
10. Amlan Kar

Papers from David Acuna's perspective

Ordered by how strongly the researchers David Acuna trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++ (2018) — 442 citations
• Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization (2018) — 1,009 citations
• Meta-Sim: Learning to Generate Synthetic Datasets (2019) — 279 citations
• Object Instance Annotation With Deep Extreme Level Set Evolution (2019) — 87 citations
• Gated-SCNN: Gated Shape CNNs for Semantic Segmentation (2019) — 741 citations
• Devil Is in the Edges: Learning Semantic Boundaries From Noisy Annotations (2019) — 154 citations
• Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data (2018) — 316 citations