Wen-Sheng Chu

Computer Vision and Pattern Recognition · 2013–2020

A researcher in the Science corpus: 3 papers; trusts 239 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it; #7,550 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 25 citations into it
• Generative Models — 24 citations into it
• Experimental and Cognitive Psychology — 12 citations into it; #1,910 of 9,731 by trust here, #4,180 by citations
• Optimization & Training — 8 citations into it
• Signal Processing — 6 citations into it
• Cognitive Neuroscience — 5 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Wen-Sheng Chu trusts overall

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

1. Thorsten Joachims
2. Chih-Jen Lin
3. Geoffrey E. Hinton
4. Olivier Chapelle
5. Alex Krizhevsky
6. Karen Simonyan
7. Andrew Zisserman
8. Yoshua Bengio
9. Kaiming He
10. Diederik P. Kingma

Who trusts Wen-Sheng Chu overall

1. Gabriella Csurka
2. Zheng Xu
3. Wen Li
4. Punarjay Chakravarty
5. Brais Martínez
6. Evangelos Sariyanidi
7. Nikolaos Sarafianos
8. Fan Qi
9. Tinne Tuytelaars
10. Michel François Valstar

Papers from Wen-Sheng Chu's perspective

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

Their papers

• Selective Transfer Machine for Personalized Facial Action Unit Detection (2013) — 310 citations
• Few-Shot Adaptation of Generative Adversarial Networks (2020) — 112 citations
• Deep Region and Multi-label Learning for Facial Action Unit Detection (2016) — 347 citations