Jianpeng Zhang

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 7 papers; trusts 501 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #14,605 of 21,575 by trust here, #11,286 by citations
• Artificial Intelligence — 98 citations into it; #26,929 of 35,345 by trust here, #18,586 by citations
• Model Architectures — 37 citations into it
• Radiology, Nuclear Medicine and Imaging — 36 citations into it
• Optimization & Training — 16 citations into it
• Biophysics — 10 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Media Technology — 8 citations into it

Whom Jianpeng Zhang trusts overall

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

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

Who trusts Jianpeng Zhang overall

1. Yutong Xie
2. Ali Narin
3. Ali Hatamizadeh
4. Michael James Horry
5. Xiaohong Huang
6. Yong Xia
7. Feng Shi
8. Ange Lou
9. Onat Dalmaz
10. Dong Yang

Papers from Jianpeng Zhang's perspective

Ordered by how strongly the researchers Jianpeng Zhang 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

• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation (2021) — 715 citations
• Continual Self-Supervised Learning: Towards Universal Multi-Modal Medical Data Representation Learning (2023) — 56 citations
• COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection (2020) — 464 citations
• Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019) — 439 citations
• Medical image classification using synergic deep learning (2019) — 374 citations
• A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification (2019) — 340 citations
• Attention Residual Learning for Skin Lesion Classification (2019) — 503 citations