Kevin Lai

Computer Vision and Pattern Recognition · with Bo · 2011–2014

A researcher in the Science corpus: 6 papers; trusts 384 researchers and is trusted by 1,072 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #1,299 of 21,575 by trust here, #2,435 by citations
• Artificial Intelligence — 35 citations into it; #11,291 of 35,345 by trust here, #11,490 by citations
• Aerospace Engineering — 23 citations into it; #313 of 6,421 by trust here, #414 by citations
• Representation & Self-Supervised Learning — 23 citations into it; #923 of 3,145 by trust here, #1,741 by citations
• Evaluation & Benchmarks — 13 citations into it; #603 of 3,147 by trust here, #899 by citations
• Model Architectures — 9 citations into it
• Geology — 8 citations into it; #101 of 687 by trust here, #267 by citations

Whom Kevin Lai trusts overall

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

1. Pedro F. Felzenszwalb
2. David G. Lowe
3. Xiaofeng Ren
4. Navneet Dalal
5. Liefeng Bo
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Li Fei-Fei
9. Jitendra Malik
10. Yann LeCun

Who trusts Kevin Lai overall

1. Liefeng Bo
2. Xiaofeng Ren
3. Michael Firman
4. Bo Li
5. Dieter Fox
6. Shuran Song
7. Hema Swetha Koppula
8. Saurabh Gupta
9. Sudeep Pillai
10. Richard Socher

Papers from Kevin Lai's perspective

Ordered by how strongly the researchers Kevin Lai 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A large-scale hierarchical multi-view RGB-D object dataset (2011) — 1,545 citations
• Unsupervised feature learning for 3D scene labeling (2014) — 382 citations
• Detection-based object labeling in 3D scenes (2012) — 210 citations
• Sparse distance learning for object recognition combining RGB and depth information (2011) — 230 citations
• Object recognition with hierarchical kernel descriptors (2011) — 263 citations
• RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark (2013) — 72 citations