Tong Shen

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 403 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 60 citations into it; #14,181 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 43 citations into it; #22,965 of 35,345 by trust here, #20,836 by citations
• Representation & Self-Supervised Learning — 9 citations into it; #2,326 of 3,145 by trust here, #2,587 by citations
• Model Architectures — 6 citations into it

Whom Tong Shen trusts overall

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

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

Who trusts Tong Shen overall

1. Lukas Hoyer
2. Andrea Maracani
3. Guolei Sun
4. Weide Liu
5. Amita Kamath
6. Yahao Liu
7. Dengxin Dai
8. Luc Van Gool
9. Oren Nuriel
10. Wenguan Wang

Papers from Tong Shen's perspective

Ordered by how strongly the researchers Tong Shen 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. 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

• Bootstrapping the Performance of Webly Supervised Semantic Segmentation (2018) — 72 citations
• Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation (2020) — 340 citations