Kai Xu

Computer Vision and Pattern Recognition · with Xiong · 2010–2018

A researcher in the Science corpus: 5 papers; trusts 132 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it; #14,865 of 21,575 by trust here, #12,172 by citations
• Computational Mechanics — 64 citations into it; #7,405 of 10,001 by trust here, #2,942 by citations
• Computer Graphics and Computer-Aided Design — 31 citations into it; #1,162 of 1,584 by trust here, #592 by citations
• Aerospace Engineering — 16 citations into it
• Generative Models — 13 citations into it
• Artificial Intelligence — 10 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Kai Xu trusts overall

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

1. Richard Socher
2. Geoffrey E. Hinton
3. Andrew Y. Ng
4. Sagi Katz
5. Evangelos Kalogerakis
6. Alex Krizhevsky
7. Siddhartha Chaudhuri
8. Yoshua Bengio
9. Kaiming He
10. Diederik P. Kingma

Who trusts Kai Xu overall

1. Zhiqin Chen
2. Qi-Xing Huang
3. Li Yi
4. Zhaoliang Lun
5. Yunhai Wang
6. Hao Su
7. Leonidas J. Guibas
8. Hao Zhang
9. Kan Guo
10. Vladimir G. Kim

Papers from Kai Xu's perspective

Ordered by how strongly the researchers Kai Xu 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• 3D Shape Segmentation and Labeling via Extreme Learning Machine (2014) — 93 citations
• Deformation-driven shape correspondence via shape recognition (2017) — 176 citations
• SCORES (2018) — 124 citations
• Data-driven contextual modeling for 3D scene understanding (2016) — 58 citations
• Style-content separation by anisotropic part scales (2010) — 155 citations