Hongyu Wang

Computer Vision and Pattern Recognition · with Wang · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 835 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 124 citations into it; #4,901 of 21,575 by trust here, #6,281 by citations
• Media Technology — 27 citations into it; #2,243 of 3,621 by trust here, #1,079 by citations
• Artificial Intelligence — 21 citations into it
• Model Architectures — 14 citations into it
• Sensory Systems — 13 citations into it
• Cognitive Neuroscience — 11 citations into it; #3,098 of 10,852 by trust here, #3,702 by citations
• Optimization & Training — 9 citations into it
• Computational Mechanics — 5 citations into it

Whom Hongyu Wang trusts overall

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

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

Who trusts Hongyu Wang overall

1. Deng-Ping Fan
2. Wenguan Wang
3. Jiangjiang Liu
4. Huchuan Lu
5. Ting Zhao
6. Zhe Wu
7. Tiantian Wang
8. Nian Liu
9. Xuebin Qin
10. Youwei Pang

Papers from Hongyu Wang's perspective

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

Their papers

• End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks (2016) — 149 citations
• Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection (2017) — 790 citations
• Learning Uncertain Convolutional Features for Accurate Saliency Detection (2017) — 447 citations