Andrew Markham

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 12 papers; trusts 1,020 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 371 citations into it; #20,993 of 21,575 by trust here, #6,485 by citations
• Aerospace Engineering — 178 citations into it
• Computational Mechanics — 175 citations into it
• Geology — 108 citations into it
• Artificial Intelligence — 95 citations into it
• 3D Vision & Pose — 93 citations into it
• Computer Graphics and Computer-Aided Design — 55 citations into it
• Generative Models — 52 citations into it

Whom Andrew Markham trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. David G. Lowe
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Ross B. Girshick

Who trusts Andrew Markham overall

1. Muhamad Risqi U. Saputra
2. Salvatore Cuomo

Papers from Andrew Markham's perspective

Ordered by how strongly the researchers Andrew Markham 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (2019) — 2,046 citations
• VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem (2017) — 401 citations
• 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations (2018) — 28 citations
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017) — 208 citations
• IONet: Learning to Cure the Curse of Drift in Inertial Odometry (2018) — 383 citations
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (2021) — 400 citations
• SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration (2020) — 402 citations
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (2019) — 390 citations
• Distilling Knowledge From a Deep Pose Regressor Network (2019) — 118 citations
• 3D Object Dense Reconstruction from a Single Depth View (2018) — 29 citations
• Visual SLAM and Structure from Motion in Dynamic Environments (2018) — 407 citations
• Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction (2018) — 156 citations