Bin Fan

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 6 papers; trusts 783 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 160 citations into it; #10,231 of 21,575 by trust here, #11,950 by citations
• Computational Mechanics — 60 citations into it; #9,904 of 10,001 by trust here, #3,029 by citations
• Artificial Intelligence — 49 citations into it
• Aerospace Engineering — 44 citations into it; #2,745 of 6,421 by trust here, #4,388 by citations
• Geology — 41 citations into it; #680 of 687 by trust here, #300 by citations
• Model Architectures — 26 citations into it
• Environmental Engineering — 18 citations into it
• Media Technology — 12 citations into it

Whom Bin Fan trusts overall

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

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

Who trusts Bin Fan overall

1. Yurun Tian
2. M. Tyszkiewicz
3. Pascal Fua
4. Eduard Trulls
5. Ankit Goyal
6. Xu Ma
7. Jérôme Revaud
8. Jiayi Ma
9. Ondrej Miksik
10. Haozhe Xie

Papers from Bin Fan's perspective

Ordered by how strongly the researchers Bin 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• SOSNet: Second Order Similarity Regularization for Local Descriptor Learning (2019) — 385 citations
• Local Intensity Order Pattern for feature description (2011) — 347 citations
• Relation-Shape Convolutional Neural Network for Point Cloud Analysis (2019) — 1,032 citations
• AugFPN: Improving Multi-Scale Feature Learning for Object Detection (2019) — 495 citations
• DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (2019) — 299 citations
• L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space (2017) — 574 citations