Ji Zhao

Computer Vision and Pattern Recognition · IEEE Transactions on Image Processing · 2014–2018

A researcher in the Science corpus: 4 papers; trusts 212 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #12,568 of 21,575 by trust here, #12,419 by citations
• Aerospace Engineering — 44 citations into it; #3,782 of 6,421 by trust here, #3,911 by citations
• Artificial Intelligence — 12 citations into it
• Computational Mechanics — 11 citations into it; #9,189 of 10,001 by trust here, #7,501 by citations
• Geology — 10 citations into it; #621 of 687 by trust here, #675 by citations
• Statistical Learning Theory — 10 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Statistics and Probability — 6 citations into it

Whom Ji Zhao trusts overall

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

1. David G. Lowe
2. Arthur P. Dempster
3. Andrew Zisserman
4. Serge J. Belongie
5. Andrea Vedaldi
6. Radford M. Neal
7. Andriy Myronenko
8. Jitendra Malik
9. Geoffrey E. Hinton
10. Alex Krizhevsky

Who trusts Ji Zhao overall

1. Jiayi Ma
2. Xingyu Jiang
3. Guoquan Huang
4. Yansheng Li

Papers from Ji Zhao's perspective

Ordered by how strongly the researchers Ji Zhao 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. Attention is All you Need (Neural Information Processing Systems)
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

• PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features (2018) — 304 citations
• Non-Rigid Point Set Registration by Preserving Global and Local Structures (2015) — 330 citations
• Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming (2015) — 457 citations
• Robust Point Matching via Vector Field Consensus (2014) — 551 citations