Zongsheng Yue

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 570 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 156 citations into it; #13,548 of 21,575 by trust here, #12,992 by citations
• Generative Models — 55 citations into it
• Media Technology — 51 citations into it
• Artificial Intelligence — 28 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Computational Mechanics — 12 citations into it
• Computer Graphics and Computer-Aided Design — 12 citations into it

Whom Zongsheng Yue trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Radford M. Neal
6. Yoshua Bengio
7. Jimmy Ba
8. Kai Zhang
9. Jonathan Ho
10. Yann LeCun

Who trusts Zongsheng Yue overall

1. Syed Waqas Zamir
2. Tao Yang
3. Chong Mou
4. X. Lin
5. Zhendong Wang
6. Aditya Arora
7. Jie Xiao
8. Peiran Ren
9. Yufei Wang
10. Zhengzhong Tu

Papers from Zongsheng Yue's perspective

Ordered by how strongly the researchers Zongsheng Yue 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Variational Denoising Network: Toward Blind Noise Modeling and Removal (2019) — 374 citations
• Exploiting Diffusion Prior for Real-World Image Super-Resolution (2023) — 655 citations
• ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (2023) — 569 citations