Kun-Juan Wei

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 756 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #16,451 of 21,575 by trust here, #11,083 by citations
• Artificial Intelligence — 91 citations into it; #26,871 of 35,345 by trust here, #16,471 by citations
• Representation & Self-Supervised Learning — 44 citations into it
• Meta & Continual Learning — 24 citations into it
• Radiology, Nuclear Medicine and Imaging — 13 citations into it; #2,349 of 3,265 by trust here, #2,462 by citations
• Optimization & Training — 8 citations into it
• Efficiency & Compression — 7 citations into it
• Generative Models — 7 citations into it

Whom Kun-Juan Wei trusts overall

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

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

Who trusts Kun-Juan Wei overall

1. Yanan Gu
2. Cheng Deng
3. Pravendra Singh
4. Xu Yang
5. Pratik Mazumder
6. Liyuan Wang

Papers from Kun-Juan Wei's perspective

Ordered by how strongly the researchers Kun-Juan Wei 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition (2019) — 96 citations
• Class-Incremental Instance Segmentation via Multi-Teacher Networks (2021) — 32 citations
• Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency (2022) — 106 citations
• Lifelong Zero-Shot Learning (2020) — 74 citations