Chen Kong

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 5 papers; trusts 586 researchers and is trusted by 451 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it; #1,986 of 21,575 by trust here, #4,584 by citations
• Computational Mechanics — 43 citations into it; #2,798 of 10,001 by trust here, #2,509 by citations
• 3D Vision & Pose — 36 citations into it; #574 of 689 by trust here, #639 by citations
• Aerospace Engineering — 30 citations into it; #4,618 of 6,421 by trust here, #5,172 by citations
• Artificial Intelligence — 25 citations into it
• Computer Graphics and Computer-Aided Design — 17 citations into it; #767 of 1,584 by trust here, #605 by citations
• Generative Models — 17 citations into it
• Multimodal Learning — 16 citations into it; #317 of 1,211 by trust here, #539 by citations

Whom Chen Kong trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. João Carreira
6. Ross B. Girshick
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Jitendra Malik

Who trusts Chen Kong overall

1. Mateusz Malinowski
2. David F. Harwath
3. Stephan R. Richter
4. Mohit Bansal
5. Sanja Fidler
6. Maxim Tatarchenko
7. Marcus Rohrbach
8. Hao Hao Tan
9. Yiyi Liao
10. Dahua Lin

Papers from Chen Kong's perspective

Ordered by how strongly the researchers Chen Kong 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• What Are You Talking About? Text-to-Image Coreference (2014) — 223 citations
• Visual Semantic Search: Retrieving Videos via Complex Textual Queries (2014) — 159 citations
• Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction (2017) — 462 citations
• Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image (2017) — 62 citations
• Image2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017) — 138 citations