Na Dong

Computer Vision and Pattern Recognition · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 223 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 60 citations into it
• Artificial Intelligence — 41 citations into it
• Optimization & Training — 5 citations into it

Whom Na Dong trusts overall

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

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

Who trusts Na Dong overall

1. Liyuan Wang
2. Xingxing Zhang

Papers from Na Dong's perspective

Ordered by how strongly the researchers Na Dong 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data (2023) — 8 citations
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection (2021)