Jiangtao Xie

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 516 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 75 citations into it; #10,271 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 52 citations into it; #32,516 of 35,345 by trust here, #17,412 by citations
• Model Architectures — 24 citations into it
• Optimization & Training — 8 citations into it
• Computational Mechanics — 7 citations into it; #8,790 of 10,001 by trust here, #6,877 by citations
• Representation & Self-Supervised Learning — 6 citations into it
• Media Technology — 5 citations into it

Whom Jiangtao Xie trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Christian Szegedy
7. X. Zhang
8. Andrew Zisserman
9. Ilya Sutskever
10. Andrea Vedaldi

Who trusts Jiangtao Xie overall

1. Heliang Zheng
2. Wenbin Li
3. Qilong Wang
4. Hossein Gholamalinezhad
5. Jianlong Fu
6. Jiebo Luo
7. Zequn Qin
8. Diganta Misra
9. Yunpeng Chen
10. Jinglin Xu

Papers from Jiangtao Xie's perspective

Ordered by how strongly the researchers Jiangtao Xie 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization (2017) — 305 citations
• Is Second-Order Information Helpful for Large-Scale Visual Recognition? (2017) — 280 citations
• Global Second-Order Pooling Convolutional Networks (2018) — 413 citations