Yanhong Zeng

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 507 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #10,232 of 21,575 by trust here, #9,079 by citations
• Generative Models — 38 citations into it; #1,495 of 1,956 by trust here, #1,336 by citations
• Computer Graphics and Computer-Aided Design — 23 citations into it
• Artificial Intelligence — 13 citations into it
• Model Architectures — 11 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Media Technology — 9 citations into it
• Biophysics — 7 citations into it

Whom Yanhong Zeng trusts overall

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

1. Alexei A. Efros
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Kaiming He
5. Alex Krizhevsky
6. Andrew Zisserman
7. Diederik P. Kingma
8. Yoshua Bengio
9. Ross B. Girshick
10. Christian Szegedy

Who trusts Yanhong Zeng overall

1. Shiyu Chang
2. Zhangyang Wang
3. Jianlong Fu
4. Jie Yang
5. Andreas Lugmayr
6. Hyungjin Chung
7. Hongyu Liu
8. Kun Yuan
9. Pengchuan Zhang
10. Zhucheng Shao

Papers from Yanhong Zeng's perspective

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

Their papers

• Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting (2019) — 471 citations
• Learning Joint Spatial-Temporal Transformations for Video Inpainting (2020) — 353 citations
• Aggregated Contextual Transformations for High-Resolution Image Inpainting (2021) — 324 citations