Jun Xie

Computer Vision and Pattern Recognition · with Geiger · 2015–2021

A researcher in the Science corpus: 2 papers; trusts 489 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 85 citations into it; #19,737 of 21,575 by trust here, #11,498 by citations
• Aerospace Engineering — 32 citations into it; #5,977 of 6,421 by trust here, #3,644 by citations
• Evaluation & Benchmarks — 25 citations into it
• Computational Mechanics — 14 citations into it
• Artificial Intelligence — 13 citations into it
• 3D Vision & Pose — 12 citations into it
• Geology — 12 citations into it
• Computer Graphics and Computer-Aided Design — 8 citations into it

Whom Jun Xie trusts overall

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

1. Andreas Geiger
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. Matthew D. Zeiler
7. Diederik P. Kingma
8. Yann LeCun
9. Yoshua Bengio
10. Antonio Torralba

Who trusts Jun Xie overall

1. Dan Barnes
2. Hassan Abu Alhaija
3. Joel Janai
4. Stephan R. Richter
5. Gerhard Neuhold
6. Yi-Hsin Chen
7. Andreas Geiger
8. Xinyu Huang
9. Axel Sauer
10. William P. Maddern

Papers from Jun Xie's perspective

Ordered by how strongly the researchers Jun 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. 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer (2015) — 172 citations
• KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D (2021) — 956 citations