Tian-Tsong Ng

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 6 papers; trusts 620 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 176 citations into it; #3,043 of 21,575 by trust here, #3,156 by citations
• Artificial Intelligence — 67 citations into it; #3,461 of 35,345 by trust here, #5,359 by citations
• Biomedical Engineering — 46 citations into it; #1,841 of 10,734 by trust here, #3,989 by citations
• Human-Computer Interaction — 41 citations into it; #1,519 of 2,253 by trust here, #914 by citations
• 3D Vision & Pose — 29 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 15 citations into it
• Media Technology — 14 citations into it

Whom Tian-Tsong Ng trusts overall

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

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

Who trusts Tian-Tsong Ng overall

1. Jun Liu
2. Hossein Rahmani
3. Amir Shahroudy
4. Pichao Wang
5. Qiuhong Ke
6. G. Wang
7. Ajmal Saeed Mian
8. Yifan Zhang
9. Fabien Baradel
10. Zehua Sun

Papers from Tian-Tsong Ng's perspective

Ordered by how strongly the researchers Tian-Tsong Ng 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. 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. 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

• NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016) — 3,015 citations
• COVERAGE — A novel database for copy-move forgery detection (2016) — 439 citations
• Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016) — 248 citations
• Multimodal Multipart Learning for Action Recognition in Depth Videos (2015) — 125 citations
• Multi-modal feature fusion for action recognition in RGB-D sequences (2014) — 51 citations
• Learning deep features for multiple object tracking by using a multi-task learning strategy (2014) — 22 citations