Zheng Shou

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 959 researchers and is trusted by 496 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 145 citations into it; #1,872 of 21,575 by trust here, #3,426 by citations
• Artificial Intelligence — 77 citations into it; #3,783 of 35,345 by trust here, #7,657 by citations
• Model Architectures — 27 citations into it; #414 of 2,278 by trust here, #942 by citations
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 7 citations into it
• Human-Computer Interaction — 6 citations into it
• Media Technology — 6 citations into it
• 3D Vision & Pose — 5 citations into it

Whom Zheng Shou trusts overall

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

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

Who trusts Zheng Shou overall

1. Jiyang Gao
2. Tianwei Lin
3. Junting Pan
4. Phuc Xuan Nguyen
5. Humam Alwassel
6. Jonghwan Mun
7. Xiyang Dai
8. Limin Wang
9. Du Tran
10. Huijuan Xu

Papers from Zheng Shou's perspective

Ordered by how strongly the researchers Zheng Shou 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs (2016) — 973 citations
• ConvNet Architecture Search for Spatiotemporal Feature Learning (2017) — 417 citations
• CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos (2017) — 586 citations
• Single Shot Temporal Action Detection (2017) — 483 citations