Jia Xu

Computer Vision and Pattern Recognition · with Chen · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 960 researchers and is trusted by 184 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #2,727 of 21,575 by trust here, #5,205 by citations
• Media Technology — 33 citations into it; #3,038 of 3,621 by trust here, #3,091 by citations
• Artificial Intelligence — 31 citations into it; #16,684 of 35,345 by trust here, #17,776 by citations
• Aerospace Engineering — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 12 citations into it
• Generative Models — 11 citations into it
• Computer Graphics and Computer-Aided Design — 7 citations into it

Whom Jia Xu trusts overall

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

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

Who trusts Jia Xu overall

1. Alexander Kolesnikov
2. Paul Vernaza
3. Yunchao Wei
4. Samuel Rota Bulò
5. George Papandreou
6. Zilong Huang
7. Qifeng Chen
8. Amy Bearman
9. Meng Tang
10. Yi Li

Papers from Jia Xu's perspective

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

Their papers

• Learning to segment under various forms of weak supervision (2015) — 190 citations
• Learning to See in the Dark (2018) — 1,542 citations
• SelFlow: Self-Supervised Learning of Optical Flow (2019) — 340 citations
• Fast Image Processing with Fully-Convolutional Networks (2017) — 356 citations
• Learning to Solve a Rubik’s Cube with a Dexterous Hand (2019) — 15 citations
• DDFlow: Learning Optical Flow with Unlabeled Data Distillation (2019) — 199 citations