Yongjian Fu

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 466 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 225 citations into it
• Computer Vision and Pattern Recognition — 212 citations into it
• Meta & Continual Learning — 98 citations into it
• Representation & Self-Supervised Learning — 65 citations into it
• Model Architectures — 34 citations into it
• Optimization & Training — 32 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Yongjian Fu trusts overall

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

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

Who trusts Yongjian Fu overall

1. Da-Wei Zhou
2. Hanbin Zhao

Papers from Yongjian Fu's perspective

Ordered by how strongly the researchers Yongjian Fu 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Memory-Efficient Class-Incremental Learning for Image Classification (2020) — 108 citations
• What and Where: Learn to Plug Adapters via NAS for Multidomain Learning (2020) — 21 citations
• MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning (2020) — 151 citations
• Referring Expression Comprehension via Cross-Level Multi-Modal Fusion (2022) — 5 citations
• Few-Shot Class-Incremental Learning via Feature Space Composition (2020) — 6 citations