Mei Wang

Computer Vision and Pattern Recognition · with Deng · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 1,698 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 328 citations into it; #6,711 of 21,575 by trust here, #4,926 by citations
• Artificial Intelligence — 158 citations into it; #11,696 of 35,345 by trust here, #10,319 by citations
• Signal Processing — 107 citations into it; #4,374 of 7,572 by trust here, #1,820 by citations
• Representation & Self-Supervised Learning — 74 citations into it
• Model Architectures — 29 citations into it
• Generative Models — 24 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Optimization & Training — 15 citations into it

Whom Mei Wang trusts overall

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

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

Who trusts Mei Wang overall

1. Weihong Deng
2. Zhongxin Bai
3. Haohan Wang
4. Vinod Kumar Kurmi
5. Yanyang Gu
6. Yehui Tang
7. Zaid Khan
8. Yaobin Zhang
9. Marco Fiorucci
10. Radek Daněček

Papers from Mei Wang's perspective

Ordered by how strongly the researchers Mei 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep Visual Domain Adaptation: A Survey (2018) — 2,330 citations
• Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data (2019) — 129 citations
• Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network (2018) — 390 citations
• Deep Face Recognition: A Survey (2018) — 1,446 citations
• Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition (2020) — 74 citations