Yu Zhang

Computer Vision and Pattern Recognition · with Wu · 2014–2016

A researcher in the Science corpus: 5 papers; trusts 498 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 146 citations into it; #14,452 of 21,575 by trust here, #9,187 by citations
• Artificial Intelligence — 71 citations into it; #27,688 of 35,345 by trust here, #25,040 by citations
• Aerospace Engineering — 17 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Media Technology — 14 citations into it; #3,223 of 3,621 by trust here, #2,178 by citations
• Model Architectures — 6 citations into it

Whom Yu Zhang trusts overall

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

1. Ross B. Girshick
2. Karen Simonyan
3. Andrew Zisserman
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Kaiming He
7. David G. Lowe
8. Yann LeCun
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Yu Zhang overall

1. Jiuxiang Gu
2. Yuxin Peng
3. Xiao Liu
4. Abhimanyu Dubey
5. Tianshui Chen
6. Samitha Herath
7. Zhenhua Wang
8. Limin Wang
9. Xiangteng He
10. Xiu-Shen Wei

Papers from Yu Zhang's perspective

Ordered by how strongly the researchers Yu Zhang 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

• Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation (2016) — 187 citations
• Exploit Bounding Box Annotations for Multi-Label Object Recognition (2015) — 181 citations
• Towards Good Practices for Action Video Encoding (2014) — 75 citations
• Action Recognition in Still Images With Minimum Annotation Efforts (2016) — 84 citations
• Weakly Supervised Fine-Grained Image Categorization (2015) — 53 citations