Loong Fah Cheong

Computer Vision and Pattern Recognition · 2009–2020

A researcher in the Science corpus: 4 papers; trusts 470 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #8,719 of 21,575 by trust here, #8,122 by citations
• Media Technology — 22 citations into it
• Artificial Intelligence — 13 citations into it; #30,528 of 35,345 by trust here, #15,236 by citations
• Model Architectures — 12 citations into it
• Generative Models — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Loong Fah Cheong trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. David G. Lowe
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Robby T. Tan
7. Yoshua Bengio
8. Jimmy Ba
9. Karen Simonyan
10. Zhou Wang

Who trusts Loong Fah Cheong overall

1. Ozan Özdenizci
2. Jeya Maria Jose Valanarasu
3. Robert A. Legenstein
4. Ruoteng Li
5. Rajeev Yasarla
6. Xia Li
7. Yu-Gang Jiang
8. Xiaodong Yang
9. Lixin Duan

Papers from Loong Fah Cheong's perspective

Ordered by how strongly the researchers Loong Fah Cheong 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. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Hierarchical spatio-temporal context modeling for action recognition (2009) — 450 citations
• All in One Bad Weather Removal Using Architectural Search (2020) — 397 citations
• Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning (2019) — 459 citations
• Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network (2017) — 60 citations