Gang Wang

Computer Vision and Pattern Recognition · with Liu · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 988 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 481 citations into it; #4,028 of 21,575 by trust here, #2,536 by citations
• Artificial Intelligence — 269 citations into it; #17,620 of 35,345 by trust here, #18,586 by citations
• Model Architectures — 71 citations into it; #508 of 2,278 by trust here, #439 by citations
• Optimization & Training — 53 citations into it
• Signal Processing — 52 citations into it
• Biomedical Engineering — 45 citations into it; #414 of 10,734 by trust here, #1,978 by citations
• Human-Computer Interaction — 43 citations into it
• Representation & Self-Supervised Learning — 40 citations into it

Whom Gang Wang trusts overall

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

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

Who trusts Gang Wang overall

1. Jun Liu
2. Hossein Rahmani
3. Amir Shahroudy
4. G. Wang
5. Qiuhong Ke
6. Ajmal Saeed Mian
7. Mengyuan Liu
8. Jingkuan Song
9. Pichao Wang
10. Zehua Sun

Papers from Gang Wang's perspective

Ordered by how strongly the researchers Gang 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
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

• Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition (2016) — 1,227 citations
• Deep hashing for compact binary codes learning (2015) — 550 citations
• Recent advances in convolutional neural networks (2015) — 6,013 citations
• Semantic Segmentation With Context Encoding and Multi-Path Decoding (2020) — 148 citations
• Multimodal Multipart Learning for Action Recognition in Depth Videos (2015) — 125 citations