Bo Yang

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 613 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 247 citations into it
• Computational Mechanics — 155 citations into it
• Aerospace Engineering — 109 citations into it
• Geology — 98 citations into it
• 3D Vision & Pose — 87 citations into it
• Computer Graphics and Computer-Aided Design — 54 citations into it
• Generative Models — 51 citations into it
• Environmental Engineering — 39 citations into it

Whom Bo Yang trusts overall

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

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

Who trusts Bo Yang overall

1. Xian-Feng Han
2. Dave Zhenyu Chen
3. Xiaoqiang Yan
4. Edward Wagstaff
5. Wentao Yuan
6. Lei Han
7. Zhihua Wang
8. Zitian Huang
9. Dan Wang
10. Thang Vu

Papers from Bo Yang's perspective

Ordered by how strongly the researchers Bo Yang 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. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (2019) — 2,046 citations
• 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations (2018) — 28 citations
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017) — 208 citations
• SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration (2020) — 402 citations
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (2019) — 390 citations
• 3D Object Dense Reconstruction from a Single Depth View (2018) — 29 citations
• Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction (2018) — 156 citations