ZongYuan Ge

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 10 papers; trusts 656 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 214 citations into it; #11,137 of 21,575 by trust here, #4,212 by citations
• Artificial Intelligence — 190 citations into it; #29,815 of 35,345 by trust here, #14,181 by citations
• Optimization & Training — 43 citations into it
• Model Architectures — 31 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Neural Architecture Search & AutoML — 25 citations into it
• Radiology, Nuclear Medicine and Imaging — 15 citations into it

Whom ZongYuan Ge trusts overall

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

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

Who trusts ZongYuan Ge overall

1. Poojan Oza
2. Da-Wei Zhou
3. Chuanxing Geng
4. Guangyao Chen
5. Jianpeng Zhang
6. Kuniaki Saito
7. Hongjie Zhang
8. Zhen Fang
9. Mengyu Wang
10. Xin Sun

Papers from ZongYuan Ge's perspective

Ordered by how strongly the researchers ZongYuan Ge 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Simple online and realtime tracking (2016) — 4,063 citations
• Generative OpenMax for Multi-Class Open Set Classification (2017) — 469 citations
• DeepFruits: A Fruit Detection System Using Deep Neural Networks (2016) — 1,000 citations
• Mutual consistency learning for semi-supervised medical image segmentation (2021) — 418 citations
• Semi-supervised Left Atrium Segmentation with Mutual Consistency Training (2021) — 348 citations
• Robust early-learning: Hindering the memorization of noisy labels (2021) — 338 citations
• One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting (2020) — 81 citations
• Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification (2020) — 131 citations
• Hierarchical Neural Architecture Search for Deep Stereo Matching (2020) — 429 citations
• Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images (2017) — 142 citations