Lap-Fai Craig Yu

Computer Vision and Pattern Recognition · 2011–2021

A researcher in the Science corpus: 7 papers; trusts 432 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 181 citations into it; #8,271 of 21,575 by trust here, #7,499 by citations
• Aerospace Engineering — 60 citations into it; #1,576 of 6,421 by trust here, #1,602 by citations
• Artificial Intelligence — 40 citations into it
• 3D Vision & Pose — 37 citations into it; #417 of 689 by trust here, #421 by citations
• Computational Mechanics — 28 citations into it; #3,941 of 10,001 by trust here, #1,536 by citations
• Computer Graphics and Computer-Aided Design — 27 citations into it
• Evaluation & Benchmarks — 19 citations into it; #1,731 of 3,147 by trust here, #1,921 by citations
• Media Technology — 10 citations into it

Whom Lap-Fai Craig Yu trusts overall

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

1. Geoffrey E. Hinton
2. Jun S. Liu
3. Antonio Torralba
4. Kaiming He
5. Alex Krizhevsky
6. Ross B. Girshick
7. Jitendra Malik
8. Yoshua Bengio
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Lap-Fai Craig Yu overall

1. Ankur Handa
2. Binh-Son Hua
3. Manolis Savva
4. Matthew Fisher
5. Viorica Patraucean
6. David Raposo
7. Chenfanfu Jiang
8. Angel X. Chang
9. Mike Roberts
10. Quang-Hieu Pham

Papers from Lap-Fai Craig Yu's perspective

Ordered by how strongly the researchers Lap-Fai Craig Yu 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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
• Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars (2017) — 86 citations
• A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation (2016) — 46 citations
• A Review on Virtual Reality Skill Training Applications (2021) — 400 citations
• RGB-D Object-to-CAD Retrieval (2018) — 18 citations
• RGB-D to CAD Retrieval with ObjectNN Dataset (2017) — 20 citations
• Make it home: automatic optimization of furniture arrangement (2011) — 468 citations