Chaohui Wang

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 864 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 151 citations into it; #3,501 of 21,575 by trust here, #4,344 by citations
• Generative Models — 31 citations into it
• Aerospace Engineering — 30 citations into it; #2,736 of 6,421 by trust here, #2,026 by citations
• Media Technology — 27 citations into it; #1,810 of 3,621 by trust here, #486 by citations
• Artificial Intelligence — 22 citations into it
• 3D Vision & Pose — 17 citations into it; #264 of 689 by trust here, #612 by citations
• Model Architectures — 8 citations into it

Whom Chaohui Wang trusts overall

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

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

Who trusts Chaohui Wang overall

1. Zhile Ren
2. Jun Xie
3. David Stutz
4. Michael Firman
5. Hyeonseob Nam
6. Matthias Mueller
7. Ran Tao
8. Chaozheng Wu
9. Shariq Farooq Bhat
10. Shuran Song

Papers from Chaohui Wang's perspective

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

• Deep Ordinal Regression Network for Monocular Depth Estimation (2018) — 2,009 citations
• MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking (2015) — 639 citations
• Joint 3D Object and Layout Inference from a Single RGB-D Image (2015) — 56 citations
• Perceptual Adversarial Networks for Image-to-Image Transformation (2017) — 392 citations