Shuo Chen

Computer Vision and Pattern Recognition · with Li · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 395 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #18,991 of 21,575 by trust here, #8,122 by citations
• Artificial Intelligence — 89 citations into it; #31,023 of 35,345 by trust here, #11,624 by citations
• Model Architectures — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Radiology, Nuclear Medicine and Imaging — 16 citations into it
• Optimization & Training — 10 citations into it; #2,396 of 2,601 by trust here, #2,112 by citations
• Generative Models — 8 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Shuo Chen trusts overall

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

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

Who trusts Shuo Chen overall

1. Eunbyung Park
2. Christian Garbin
3. Ping Luo
4. Ruijia Xu
5. Jacob Hoxbroe Jeppesen

Papers from Shuo Chen's perspective

Ordered by how strongly the researchers Shuo Chen 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. 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. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection (2020) — 1,924 citations
• Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift (2018) — 331 citations
• Contrastive Embedding for Generalized Zero-Shot Learning (2021) — 270 citations