Songlin Dong

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 161 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #13,866 of 21,575 by trust here, #6,206 by citations
• Artificial Intelligence — 62 citations into it; #22,330 of 35,345 by trust here, #8,706 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Meta & Continual Learning — 25 citations into it; #714 of 1,041 by trust here, #236 by citations
• Efficiency & Compression — 11 citations into it
• Model Architectures — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Songlin Dong trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Xiaoyu Tao
5. Oriol Vinyals
6. Yoshua Bengio
7. Yann LeCun
8. Jia Deng
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Songlin Dong overall

1. Da-Wei Zhou
2. Song Tian
3. Han-Jia Ye
4. Yibo Yang
5. Zhixiang Chi
6. Ayan Kumar Bhunia
7. Liyuan Wang
8. Jingwen Ye
9. De-chuan Zhan
10. Li Gu

Papers from Songlin Dong's perspective

Ordered by how strongly the researchers Songlin 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Few-Shot Class-Incremental Learning (2020) — 556 citations
• Few-Shot Class-Incremental Learning via Relation Knowledge Distillation (2021) — 224 citations