Quanfu Fan

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 8 papers; trusts 1,394 researchers and is trusted by 346 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 271 citations into it; #2,191 of 21,575 by trust here, #2,373 by citations
• Artificial Intelligence — 185 citations into it; #8,221 of 35,345 by trust here, #7,858 by citations
• Model Architectures — 69 citations into it; #985 of 2,278 by trust here, #994 by citations
• Evaluation & Benchmarks — 32 citations into it
• Efficiency & Compression — 27 citations into it
• Optimization & Training — 25 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Aerospace Engineering — 12 citations into it

Whom Quanfu Fan trusts overall

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

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

Who trusts Quanfu Fan overall

1. Yancheng Bai
2. Yanyu Li
3. Bharat Singh
4. Wei David Liu
5. Chun-Fu Chen
6. Shifeng Zhang
7. Yulin Wang
8. Jianwei Yang
9. Yongqiang Zhang
10. Yuda Song

Papers from Quanfu Fan's perspective

Ordered by how strongly the researchers Quanfu Fan trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection (2016) — 1,559 citations
• Moments in Time Dataset: One Million Videos for Event Understanding (2018) — 632 citations
• CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (2021) — 2,175 citations
• RegionViT: Regional-to-Local Attention for Vision Transformers (2021) — 255 citations
• Random Laplace Feature Maps for Semigroup Kernels on Histograms (2014) — 54 citations
• Adversarial T-Shirt! Evading Person Detectors in a Physical World (2019) — 428 citations
• AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition (2021) — 67 citations
• Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors (2020) — 12 citations