Jun Shi

Computer Vision and Pattern Recognition · 3 papers · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 90 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it
• Artificial Intelligence — 37 citations into it
• Media Technology — 13 citations into it
• Psychiatry and Mental health — 10 citations into it
• Neurology — 9 citations into it
• Model Architectures — 8 citations into it
• Biomedical Engineering — 7 citations into it

Whom Jun Shi trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Chao Dong
5. Alex Krizhevsky
6. Chih-Jen Lin
7. Heung-Il Suk
8. Yann LeCun
9. Matthew D. Zeiler
10. X. Zhang

Who trusts Jun Shi overall

1. Senzhang Wang
2. Chunfeng Lian
3. Mufti Mahmud
4. Junhao Wen
5. Jiannong Cao
6. Mingxia Liu
7. M. S. Kaiser

Papers from Jun Shi's perspective

Ordered by how strongly the researchers Jun Shi 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020) — 1,123 citations
• Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018) — 379 citations
• Super-resolution reconstruction of MR image with a novel residual learning network algorithm (2018) — 97 citations