Lukas Hoyer

Computer Vision and Pattern Recognition · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 1,120 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 193 citations into it; #19,344 of 21,575 by trust here, #18,266 by citations
• Artificial Intelligence — 152 citations into it; #31,520 of 35,345 by trust here, #28,481 by citations
• Radiology, Nuclear Medicine and Imaging — 45 citations into it
• Representation & Self-Supervised Learning — 44 citations into it
• Model Architectures — 26 citations into it
• Optimization & Training — 16 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Lukas Hoyer trusts overall

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

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

Who trusts Lukas Hoyer overall

1. Xiangtai Li
2. Junshi Xia
3. Dengxin Dai
4. Henghui Ding
5. Naoto Yokoya

Papers from Lukas Hoyer's perspective

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

Their papers

• HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation (2022) — 328 citations
• MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation (2022) — 375 citations
• DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation (2021) — 618 citations