Chen-Hsuan Lin

Computer Vision and Pattern Recognition · with Kong · 2017–2023

A researcher in the Science corpus: 4 papers; trusts 297 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #7,145 of 21,575 by trust here, #4,955 by citations
• Computer Graphics and Computer-Aided Design — 58 citations into it; #766 of 1,584 by trust here, #219 by citations
• Computational Mechanics — 54 citations into it; #2,509 of 10,001 by trust here, #842 by citations
• 3D Vision & Pose — 36 citations into it; #573 of 689 by trust here, #639 by citations
• Aerospace Engineering — 18 citations into it; #4,617 of 6,421 by trust here, #5,172 by citations
• Artificial Intelligence — 12 citations into it

Whom Chen-Hsuan Lin trusts overall

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

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

Who trusts Chen-Hsuan Lin overall

1. Stephan R. Richter
2. Kang Yin
3. Maxim Tatarchenko
4. Heewoo Jun
5. Matheus Gadelha
6. Bo Yang
7. Alex Nichol
8. Tong He
9. Kyle Genova
10. Zitian Huang

Papers from Chen-Hsuan Lin's perspective

Ordered by how strongly the researchers Chen-Hsuan Lin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction (2017) — 462 citations
• Magic3D: High-Resolution Text-to-3D Content Creation (2022) — 1,645 citations
• Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image (2017) — 62 citations
• Neuralangelo: High-Fidelity Neural Surface Reconstruction (2023) — 628 citations