Sijie Song

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 132 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 75 citations into it; #6,935 of 21,575 by trust here, #7,027 by citations
• Artificial Intelligence — 36 citations into it; #21,950 of 35,345 by trust here, #23,077 by citations
• Biomedical Engineering — 21 citations into it; #3,074 of 10,734 by trust here, #3,873 by citations
• 3D Vision & Pose — 12 citations into it
• Evaluation & Benchmarks — 11 citations into it; #1,995 of 3,147 by trust here, #2,590 by citations
• Model Architectures — 9 citations into it; #1,236 of 2,278 by trust here, #1,145 by citations

Whom Sijie Song trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Yoshua Bengio
6. Sepp Hochreiter
7. Jimmy Ba
8. Yong Du
9. Karen Simonyan
10. Alex Krizhevsky

Who trusts Sijie Song overall

1. Pengfei Zhang
2. Yifan Zhang
3. Chenyang Si
4. Jun Liu
5. C. Li
6. Yisheng Song
7. Tae Soo Kim
8. Cuiling Lan
9. Lei Shi
10. Pichao Wang

Papers from Sijie Song's perspective

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

Their papers

• An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data (2016) — 1,062 citations
• PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding (2017) — 295 citations