Victor Villena-Martinez

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 602 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 183 citations into it; #16,917 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 59 citations into it
• Aerospace Engineering — 37 citations into it
• Evaluation & Benchmarks — 34 citations into it
• Model Architectures — 23 citations into it
• Computational Mechanics — 15 citations into it
• Geology — 12 citations into it
• Environmental Engineering — 9 citations into it

Whom Victor Villena-Martinez trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Rob Fergus
8. Ilya Sutskever
9. Matthew D. Zeiler
10. Karen Simonyan

Who trusts Victor Villena-Martinez overall

1. Umberto Michieli

Papers from Victor Villena-Martinez's perspective

Ordered by how strongly the researchers Victor Villena-Martinez trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• A survey on deep learning techniques for image and video semantic segmentation (2018) — 1,148 citations
• A Review on Deep Learning Techniques Applied to Semantic Segmentation (2017) — 1,387 citations