Senjian An

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 11 papers; trusts 915 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 272 citations into it; #5,512 of 21,575 by trust here, #5,565 by citations
• Artificial Intelligence — 158 citations into it; #9,903 of 35,345 by trust here, #10,207 by citations
• Biomedical Engineering — 66 citations into it; #2,227 of 10,734 by trust here, #3,650 by citations
• Model Architectures — 64 citations into it
• Representation & Self-Supervised Learning — 41 citations into it
• Human-Computer Interaction — 31 citations into it
• Signal Processing — 22 citations into it
• 3D Vision & Pose — 21 citations into it; #657 of 689 by trust here, #485 by citations

Whom Senjian An trusts overall

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

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

Who trusts Senjian An overall

1. Quynh N. Nguyen
2. Jun Liu
3. Qiuhong Ke
4. Matthias Hein
5. Yifan Zhang
6. Jeremiah Zhe Liu
7. Zehua Sun
8. Amir Shahroudy
9. Mohammed Bennamoun
10. G. Wang

Papers from Senjian An's perspective

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

Their papers

• A New Representation of Skeleton Sequences for 3D Action Recognition (2017) — 866 citations
• Deep Reconstruction Models for Image Set Classification (2015) — 172 citations
• Learning Clip Representations for Skeleton-Based 3D Action Recognition (2018) — 258 citations
• Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition (2018) — 14 citations
• Computer Vision for Human-Machine Interaction (2018) — 79 citations
• Contractive Rectifier Networks for Nonlinear Maximum Margin Classification (2015) — 12 citations
• SkeletonNet: Mining Deep Part Features for 3-D Action Recognition (2017) — 158 citations
• Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction (2016) — 40 citations
• Spatial, Structural and Temporal Feature Learning for Human Interaction Prediction (2016) — 11 citations
• Human Interaction Prediction Using Deep Temporal Features (2016) — 44 citations
• How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (2015) — 28 citations