Pengfei Fang

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 455 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 121 citations into it; #12,341 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 104 citations into it; #27,019 of 35,345 by trust here, #13,962 by citations
• Representation & Self-Supervised Learning — 38 citations into it
• Meta & Continual Learning — 36 citations into it
• Model Architectures — 17 citations into it
• Optimization & Training — 12 citations into it
• Reinforcement Learning — 10 citations into it

Whom Pengfei Fang trusts overall

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

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

Who trusts Pengfei Fang overall

1. Christian Simon
2. Ali Cheraghian
3. Piotr Koniusz
4. Mehrtash Harandi
5. Meng-Hao Guo
6. Da-Wei Zhou
7. Shafin Rahman
8. Jaya Kalla

Papers from Pengfei Fang's perspective

Ordered by how strongly the researchers Pengfei Fang 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

• Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (2021) — 233 citations
• Bilinear Attention Networks for Person Retrieval (2019) — 156 citations
• Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021) — 81 citations
• Reinforced Attention for Few-Shot Learning and Beyond (2021) — 54 citations