Philip O. Ogunbona

Computer Vision and Pattern Recognition · 2014–2018

A researcher in the Science corpus: 14 papers; trusts 1,700 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 635 citations into it; #4,169 of 21,575 by trust here, #3,251 by citations
• Artificial Intelligence — 290 citations into it; #8,306 of 35,345 by trust here, #7,062 by citations
• Biomedical Engineering — 179 citations into it; #1,157 of 10,734 by trust here, #2,835 by citations
• Human-Computer Interaction — 156 citations into it; #325 of 2,253 by trust here, #276 by citations
• Model Architectures — 47 citations into it
• 3D Vision & Pose — 46 citations into it; #531 of 689 by trust here, #430 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Evaluation & Benchmarks — 26 citations into it; #1,793 of 3,147 by trust here, #2,009 by citations

Whom Philip O. Ogunbona 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. Karen Simonyan
6. Yoshua Bengio
7. David G. Lowe
8. Yann LeCun
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Philip O. Ogunbona overall

1. Pichao Wang
2. Wanqing Li
3. Jun Liu
4. Jian Liang
5. Yonghong Hou
6. Amir Shahroudy
7. Zhimin Gao
8. Zehua Sun
9. G. Wang
10. Jing Zhang

Papers from Philip O. Ogunbona's perspective

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

Their papers

• Importance Weighted Adversarial Nets for Partial Domain Adaptation (2018) — 450 citations
• Joint Geometrical and Statistical Alignment for Visual Domain Adaptation (2017) — 536 citations
• Action Recognition From Depth Maps Using Deep Convolutional Neural Networks (2016) — 293 citations
• Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation (2014) — 45 citations
• ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring (2015) — 103 citations
• Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016) — 98 citations
• Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks (2016) — 54 citations
• Human detection from images and videos: A survey (2016) — 267 citations
• Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018) — 157 citations
• Learning Approximate Stochastic Transition Models (2017) — 25 citations
• RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017) — 384 citations
• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017) — 136 citations
• RGB-D-based action recognition datasets: A survey (2016) — 264 citations
• Economic-emission dispatch problem: A semi-definite programming approach (2014) — 58 citations