Fuyong Xing

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 778 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 187 citations into it; #9,653 of 21,575 by trust here, #7,027 by citations
• Artificial Intelligence — 90 citations into it; #15,006 of 35,345 by trust here, #8,525 by citations
• Biophysics — 49 citations into it; #681 of 1,196 by trust here, #668 by citations
• Media Technology — 31 citations into it
• Model Architectures — 27 citations into it
• Molecular Biology — 14 citations into it
• Natural Language Processing — 13 citations into it
• Aerospace Engineering — 12 citations into it

Whom Fuyong Xing trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. David G. Lowe
6. Radford M. Neal
7. Yoshua Bengio
8. Dan Claudiu Ciresan
9. Tony F. Chan
10. Ross B. Girshick

Who trusts Fuyong Xing overall

1. Peter Naylor
2. Pramila P. Shinde
3. Marick Lae
4. Massimo Salvi
5. M. O. Khairandish
6. Lin F. Yang
7. Yuxin Cui
8. Juan C. Caicedo
9. Muhammad Farooq Khan Niazi
10. Fabien Reyal

Papers from Fuyong Xing's perspective

Ordered by how strongly the researchers Fuyong Xing 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network (2017) — 343 citations
• SemiContour: A Semi-supervised Learning Approach for Contour Detection (2016) — 50 citations
• Pathologist-level interpretable whole-slide cancer diagnosis with deep learning (2019) — 318 citations
• Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification (2019) — 36 citations
• Deep Learning in Microscopy Image Analysis: A Survey (2018) — 392 citations
• An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016) — 322 citations
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016) — 479 citations