Philip David

Computer Vision and Pattern Recognition · 2002–2020

A researcher in the Science corpus: 4 papers; trusts 842 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it; #4,895 of 21,575 by trust here, #5,565 by citations
• Artificial Intelligence — 88 citations into it; #9,945 of 35,345 by trust here, #12,580 by citations
• Representation & Self-Supervised Learning — 28 citations into it; #2,175 of 3,145 by trust here, #2,066 by citations
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 10 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Cancer Research — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Philip David trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Alex Krizhevsky
5. Karen Simonyan
6. Yann LeCun
7. Ross B. Girshick
8. Matthew D. Zeiler
9. Diederik P. Kingma
10. Andrew Zisserman

Who trusts Philip David overall

1. Yuhua Chen
2. Haoyin Zhou
3. Yi-Hsuan Tsai
4. Jiahua Dong
5. Hao Su
6. Pablo Garrido
7. Christos Sakaridis
8. Umberto Michieli
9. Matteo Biasetton
10. Luc Van Gool

Papers from Philip David's perspective

Ordered by how strongly the researchers Philip David 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes (2017) — 360 citations
• Domain Adaptation for Semantic Segmentation of Urban Scenes (2017)
• SoftPOSIT: Simultaneous Pose and Correspondence Determination (2002) — 344 citations
• PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (2020) — 610 citations