Wei Zhai

Computer Vision and Pattern Recognition · with Cao · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 388 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #16,834 of 21,575 by trust here, #10,905 by citations
• Artificial Intelligence — 78 citations into it; #27,398 of 35,345 by trust here, #16,471 by citations
• Meta & Continual Learning — 39 citations into it; #850 of 1,041 by trust here, #649 by citations
• Representation & Self-Supervised Learning — 25 citations into it
• Optimization & Training — 10 citations into it
• Model Architectures — 9 citations into it
• Media Technology — 5 citations into it

Whom Wei Zhai trusts overall

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

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

Who trusts Wei Zhai overall

1. Da-Wei Zhou
2. Kai Zhu
3. Liyuan Wang
4. Binghao Liu

Papers from Wei Zhai's perspective

Ordered by how strongly the researchers Wei Zhai 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (2021) — 188 citations
• Self-Supervised Tuning for Few-Shot Segmentation (2020) — 30 citations
• Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning (2022) — 198 citations
• Deep Structure-Revealed Network for Texture Recognition (2020) — 42 citations