Qiangui Huang

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 656 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 110 citations into it; #9,071 of 21,575 by trust here, #7,340 by citations
• Artificial Intelligence — 50 citations into it
• Computational Mechanics — 37 citations into it; #2,008 of 10,001 by trust here, #1,190 by citations
• Aerospace Engineering — 26 citations into it
• 3D Vision & Pose — 22 citations into it
• Geology — 19 citations into it; #183 of 687 by trust here, #148 by citations
• Model Architectures — 19 citations into it
• Efficiency & Compression — 16 citations into it; #634 of 1,264 by trust here, #1,140 by citations

Whom Qiangui Huang trusts overall

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

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

Who trusts Qiangui Huang overall

1. Jiahui Yu
2. Shaoshuai Shi
3. Yang He
4. Chen Liu
5. Zhiyuan Zhang
6. Zhijian Liu
7. Thomas S. Huang
8. Breton L. Minnehan
9. Yangyan Li
10. Weiyue Wang

Papers from Qiangui Huang's perspective

Ordered by how strongly the researchers Qiangui Huang 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. 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation (2017) — 616 citations
• Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks (2017) — 186 citations
• Learning to Prune Filters in Convolutional Neural Networks (2018) — 186 citations
• Recurrent Slice Networks for 3D Segmentation of Point Clouds (2018) — 484 citations