Jinhong Deng

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 784 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 138 citations into it; #13,367 of 21,575 by trust here, #15,529 by citations
• Artificial Intelligence — 122 citations into it; #21,529 of 35,345 by trust here, #23,731 by citations
• Representation & Self-Supervised Learning — 46 citations into it
• Radiology, Nuclear Medicine and Imaging — 31 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 9 citations into it
• Safety, Robustness & Interpretability — 8 citations into it

Whom Jinhong Deng trusts overall

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

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

Who trusts Jinhong Deng overall

1. Yahao Liu
2. Tong Chu
3. Lukas Hoyer
4. Yu-Jhe Li
5. Wen Li
6. Lixin Duan
7. Dengxin Dai
8. Jiahua Dong

Papers from Jinhong Deng's perspective

Ordered by how strongly the researchers Jinhong Deng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Unbiased Mean Teacher for Cross-domain Object Detection (2020) — 400 citations
• Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation (2022) — 25 citations
• Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation (2022) — 50 citations
• BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-domain Semantic Segmentation (2021) — 92 citations