Eddy Ilg

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 819 researchers and is trusted by 1,768 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 212 citations into it; #962 of 21,575 by trust here, #842 by citations
• Aerospace Engineering — 50 citations into it
• 3D Vision & Pose — 40 citations into it
• Computational Mechanics — 32 citations into it
• Computer Graphics and Computer-Aided Design — 26 citations into it
• Artificial Intelligence — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it; #1,465 of 3,147 by trust here, #786 by citations
• Media Technology — 14 citations into it

Whom Eddy Ilg trusts overall

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

1. Thomas Brox
2. Jitendra Malik
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yann LeCun
7. Karen Simonyan
8. Kaiming He
9. Yoshua Bengio
10. Ross B. Girshick

Who trusts Eddy Ilg overall

1. Sergey I. Nikolenko
2. Junhwa Hur
3. Haofei Xu
4. Yi Zhu
5. Tonmoy Saikia
6. Stefan Roth
7. Simon Niklaus
8. Jia-Bin Huang
9. Andrea Vedaldi
10. Deqing Sun

Papers from Eddy Ilg's perspective

Ordered by how strongly the researchers Eddy Ilg 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• FlowNet: Learning Optical Flow with Convolutional Networks (2015) — 4,664 citations
• FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (2016) — 3,400 citations
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (2015) — 3,047 citations
• DeMoN: Depth and Motion Network for Learning Monocular Stereo (2016) — 748 citations
• Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction (2020) — 522 citations
• Lucid Data Dreaming for Object Tracking (2017) — 139 citations
• Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation (2018) — 224 citations