Qingyong Hu

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 531 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 251 citations into it
• Computational Mechanics — 180 citations into it
• Geology — 147 citations into it
• Aerospace Engineering — 130 citations into it
• Environmental Engineering — 85 citations into it
• 3D Vision & Pose — 51 citations into it
• Artificial Intelligence — 48 citations into it
• Model Architectures — 22 citations into it

Whom Qingyong Hu trusts overall

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

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

Who trusts Qingyong Hu overall

1. Ishan Misra
2. Weixiao Gao
3. Xu Yan
4. Tiange Xiang
5. Jianyun Xu
6. Jiajun Deng
7. Chenfeng Xu
8. Yaodong Cui
9. Xinge Zhu
10. Fu Li

Papers from Qingyong Hu's perspective

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

Their papers

• RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (2019) — 2,046 citations
• Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (2022) — 365 citations
• Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs (2020) — 376 citations
• SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration (2020) — 402 citations
• Deep Learning for 3D Point Clouds: A Survey (2019) — 2,282 citations
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (2019) — 390 citations