Chenxia Wu

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 318 researchers and is trusted by 85 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 57 citations into it; #3,433 of 21,575 by trust here, #3,633 by citations
• Artificial Intelligence — 19 citations into it; #24,549 of 35,345 by trust here, #23,731 by citations
• Aerospace Engineering — 14 citations into it; #949 of 6,421 by trust here, #525 by citations
• Computational Mechanics — 9 citations into it
• 3D Vision & Pose — 8 citations into it
• Biomedical Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Model Architectures — 5 citations into it

Whom Chenxia Wu trusts overall

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

1. Ivan Laptev
2. Ross B. Girshick
3. Kaiming He
4. Piotr Doll'ar
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Karen Simonyan
8. John K. Kruschke
9. Andrew Zisserman
10. Yoshua Bengio

Who trusts Chenxia Wu overall

1. Alexander Richard
2. Hilde Kuehne
3. Shaoshuai Shi
4. De-An Huang
5. Michalis Vrigkas
6. Yixin Chen
7. Pichao Wang
8. Zetong Yang
9. Juergen Gall
10. Charles Ruizhongtai Qi

Papers from Chenxia Wu's perspective

Ordered by how strongly the researchers Chenxia Wu 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Frustum PointNets for 3D Object Detection from RGB-D Data (2017) — 2,541 citations
• Watch-n-patch: Unsupervised understanding of actions and relations (2015) — 135 citations