Kaichun Mo

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 202 researchers and is trusted by 1,562 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it; #1,049 of 21,575 by trust here, #1,015 by citations
• Computational Mechanics — 43 citations into it; #153 of 10,001 by trust here, #78 by citations
• Artificial Intelligence — 34 citations into it
• Aerospace Engineering — 30 citations into it; #87 of 6,421 by trust here, #87 by citations
• Geology — 30 citations into it; #11 of 687 by trust here, #7 by citations
• Reinforcement Learning — 27 citations into it
• Environmental Engineering — 12 citations into it
• Model Architectures — 10 citations into it

Whom Kaichun Mo trusts overall

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

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

Who trusts Kaichun Mo overall

1. Charles Ruizhongtai Qi
2. Shaoshuai Shi
3. Bo Yang
4. Li Yi
5. Or Litany
6. S. Gadre
7. Yue Wang
8. Yongming Rao
9. Lyle Regenwetter
10. Leonidas J. Guibas

Papers from Kaichun Mo's perspective

Ordered by how strongly the researchers Kaichun Mo 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2016) — 18,024 citations
• SAPIEN: A SimulAted Part-Based Interactive ENvironment (2020) — 843 citations
• PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding (2018) — 953 citations
• The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation (2018) — 30 citations