Yu Liu

Computer Vision and Pattern Recognition · Information Fusion · 2015–2023

A researcher in the Science corpus: 10 papers; trusts 233 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #3,560 of 21,575 by trust here, #4,245 by citations
• Media Technology — 184 citations into it; #160 of 3,621 by trust here, #219 by citations
• Aerospace Engineering — 40 citations into it; #2,279 of 6,421 by trust here, #2,734 by citations
• Artificial Intelligence — 22 citations into it
• Biomedical Engineering — 20 citations into it; #2,082 of 10,734 by trust here, #5,712 by citations
• Computational Mechanics — 17 citations into it
• Model Architectures — 16 citations into it
• Cognitive Neuroscience — 13 citations into it

Whom Yu Liu trusts overall

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

1. Bin Yang
2. Shutao Li
3. Gemma Piella
4. Michael Elad
5. David G. Lowe
6. Stéphane Mallat
7. Costas S. Xydeas
8. Qiang Zhang
9. Vladimir S. Petrovic
10. Yann LeCun

Who trusts Yu Liu overall

1. Jiayi Ma
2. Hui Li
3. Han Xu
4. Xiaojun Wu
5. Wei Tang
6. Junjun Jiang
7. Hao Zhang
8. Xun Chen
9. Linfeng Tang
10. Fazhi He

Papers from Yu Liu's perspective

Ordered by how strongly the researchers Yu Liu 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Image Fusion With Convolutional Sparse Representation (2016) — 870 citations
• EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023) — 485 citations
• Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019) — 487 citations
• Deep learning for pixel-level image fusion: Recent advances and future prospects (2018) — 655 citations
• Infrared and visible image fusion with convolutional neural networks (2017) — 417 citations
• A medical image fusion method based on convolutional neural networks (2017) — 370 citations
• Multi-focus image fusion with a deep convolutional neural network (2017) — 1,234 citations
• Simultaneous image fusion and denoising with adaptive sparse representation (2015) — 330 citations
• A general framework for image fusion based on multi-scale transform and sparse representation (2015) — 1,343 citations
• Multi-focus image fusion with dense SIFT (2015) — 464 citations