Qiaoyong Zhong

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 178 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 148 citations into it; #7,488 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 45 citations into it; #26,659 of 35,345 by trust here, #24,417 by citations
• Biomedical Engineering — 29 citations into it; #2,297 of 10,734 by trust here, #6,773 by citations
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Human-Computer Interaction — 13 citations into it; #666 of 2,253 by trust here, #1,221 by citations
• Optimization & Training — 5 citations into it

Whom Qiaoyong Zhong trusts overall

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

1. Karen Simonyan
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Andrew Zisserman
6. Alex Krizhevsky
7. Amir Shahroudy
8. Ross B. Girshick
9. Yoshua Bengio
10. Shaoqing Ren

Who trusts Qiaoyong Zhong overall

1. Yisheng Song
2. Lei Shi
3. Zhang Zhang
4. Fanfan Ye
5. Fida Mohammad Thoker
6. Yifan Zhang
7. Liang Wang
8. Caifeng Shan
9. Shiliang Pu
10. Ke Cheng

Papers from Qiaoyong Zhong's perspective

Ordered by how strongly the researchers Qiaoyong Zhong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Skeleton-based action recognition with convolutional neural networks (2017) — 417 citations
• Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019) — 337 citations
• Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition (2020) — 348 citations
• Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation (2018) — 579 citations