Kai Zhu

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

A researcher in the Science corpus: 3 papers; trusts 342 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #16,835 of 21,575 by trust here, #11,083 by citations
• Artificial Intelligence — 78 citations into it; #27,399 of 35,345 by trust here, #16,471 by citations
• Meta & Continual Learning — 39 citations into it; #851 of 1,041 by trust here, #649 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 10 citations into it
• Model Architectures — 9 citations into it
• Media Technology — 5 citations into it

Whom Kai Zhu 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. Oriol Vinyals
9. Yann LeCun
10. Ross B. Girshick

Who trusts Kai Zhu overall

1. Da-Wei Zhou
2. Liyuan Wang
3. Chengwei Qin
4. Xingxing Zhang
5. Pei-Hung Chen
6. Binghao Liu
7. Simone Magistri
8. Han-Jia Ye
9. Grégoire Petit
10. Dipam Goswami

Papers from Kai Zhu's perspective

Ordered by how strongly the researchers Kai Zhu 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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