Sai-Kit Yeung

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 14 papers; trusts 929 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 273 citations into it; #6,741 of 21,575 by trust here, #5,711 by citations
• Computational Mechanics — 160 citations into it; #1,279 of 10,001 by trust here, #539 by citations
• Aerospace Engineering — 128 citations into it; #1,389 of 6,421 by trust here, #978 by citations
• Geology — 119 citations into it; #235 of 687 by trust here, #102 by citations
• 3D Vision & Pose — 68 citations into it; #373 of 689 by trust here, #252 by citations
• Computer Graphics and Computer-Aided Design — 45 citations into it
• Environmental Engineering — 41 citations into it; #2,588 of 5,479 by trust here, #1,430 by citations
• Artificial Intelligence — 37 citations into it

Whom Sai-Kit Yeung trusts overall

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

1. Geoffrey E. Hinton
2. Ross B. Girshick
3. Kaiming He
4. Alex Krizhevsky
5. Charles Ruizhongtai Qi
6. Karen Simonyan
7. Yann LeCun
8. David G. Lowe
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Sai-Kit Yeung overall

1. Binh-Son Hua
2. Quang-Hieu Pham
3. Ankur Handa
4. Saifullahi Aminu Bello
5. Manolis Savva
6. Zaiwei Zhang
7. Dinh C. Nguyen
8. Thang Vu
9. Angela Dai
10. Oliver Weeger

Papers from Sai-Kit Yeung's perspective

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

Their papers

• SceneNN: A Scene Meshes Dataset with aNNotations (2016) — 406 citations
• A Field Model for Repairing 3D Shapes (2016) — 122 citations
• A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation (2016) — 46 citations
• JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields (2019) — 216 citations
• GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence (2017) — 628 citations
• Rotation Invariant Convolutions for 3D Point Clouds Deep Learning (2019) — 199 citations
• Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (2019) — 1,049 citations
• ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics (2019) — 407 citations
• RGB-D Object-to-CAD Retrieval (2018) — 18 citations
• Isogeometric collocation methods for Cosserat rods and rod structures (2017) — 115 citations
• Pointwise Convolutional Neural Networks (2017) — 587 citations
• RGB-D to CAD Retrieval with ObjectNN Dataset (2017) — 20 citations
• Transmural Imaging of Ventricular Action Potentials and Post-Infarction Scars in Swine Hearts (2013) — 37 citations
• Make it home: automatic optimization of furniture arrangement (2011) — 468 citations