Qi Dong

Computer Vision and Pattern Recognition · with Gong · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 636 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it
• Artificial Intelligence — 72 citations into it; #21,658 of 35,345 by trust here, #12,387 by citations
• Representation & Self-Supervised Learning — 39 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Model Architectures — 10 citations into it
• Signal Processing — 10 citations into it
• Optimization & Training — 7 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it

Whom Qi Dong trusts overall

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

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

Who trusts Qi Dong overall

1. Fahad Sarfraz
2. K. Ruwani
3. Ziwei Liu
4. Jun Shu
5. Elahe Arani
6. M. I. M. Fernando
7. Chris Dongjoo Kim
8. Adrien Bardes
9. Yuki M. Asano
10. Zhongqi Miao

Papers from Qi Dong's perspective

Ordered by how strongly the researchers Qi Dong 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

• Unsupervised Deep Learning by Neighbourhood Discovery (2019) — 162 citations
• Class Rectification Hard Mining for Imbalanced Deep Learning (2017) — 223 citations
• Imbalanced Deep Learning by Minority Class Incremental Rectification (2018) — 368 citations