Lu Yu

Computer Vision and Pattern Recognition · with Weijer · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 461 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 151 citations into it; #8,804 of 21,575 by trust here, #4,926 by citations
• Artificial Intelligence — 130 citations into it; #17,230 of 35,345 by trust here, #7,531 by citations
• Representation & Self-Supervised Learning — 67 citations into it; #2,552 of 3,145 by trust here, #2,587 by citations
• Efficiency & Compression — 18 citations into it; #960 of 1,264 by trust here, #1,092 by citations
• Radiology, Nuclear Medicine and Imaging — 18 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Media Technology — 8 citations into it

Whom Lu Yu trusts overall

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

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

Who trusts Lu Yu overall

1. Da-Wei Zhou
2. Xialei Liu
3. Nikolaos Passalis
4. Hanbin Zhao
5. Han-Jia Ye
6. Joost van de Weijer
7. Bartłomiej Twardowski
8. Marc Masana
9. Jianqiang Xiao
10. De-chuan Zhan

Papers from Lu Yu's perspective

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

Their papers

• Semantic Drift Compensation for Class-Incremental Learning (2020) — 422 citations
• Learning Metrics From Teachers: Compact Networks for Image Embedding (2019) — 123 citations
• Continually Learning Self-Supervised Representations with Projected Functional Regularization (2021) — 58 citations
• Self-Training for Class-Incremental Semantic Segmentation (2020) — 70 citations