Klaus C. J. Dietmayer

Computer Vision and Pattern Recognition · 2011–2020

A researcher in the Science corpus: 12 papers; trusts 964 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 325 citations into it; #8,822 of 21,575 by trust here, #4,955 by citations
• Artificial Intelligence — 132 citations into it; #23,905 of 35,345 by trust here, #30,347 by citations
• Aerospace Engineering — 99 citations into it
• Automotive Engineering — 64 citations into it; #822 of 3,081 by trust here, #542 by citations
• Evaluation & Benchmarks — 53 citations into it
• Environmental Engineering — 52 citations into it; #3,870 of 5,479 by trust here, #2,998 by citations
• Computational Mechanics — 35 citations into it; #6,208 of 10,001 by trust here, #1,778 by citations
• Geology — 32 citations into it; #440 of 687 by trust here, #163 by citations

Whom Klaus C. J. Dietmayer trusts overall

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

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

Who trusts Klaus C. J. Dietmayer overall

1. Di Feng
2. Yi Xiao
3. Rohit Girdhar
4. Ishan Misra

Papers from Klaus C. J. Dietmayer's perspective

Ordered by how strongly the researchers Klaus C. J. Dietmayer 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. 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. Going deeper with convolutions

Their papers

• Point Transformer (2020) — 3,096 citations
• Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019) — 1,343 citations
• Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection (2018) — 283 citations
• Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather (2019) — 614 citations
• The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets (2018) — 62 citations
• Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods (2013) — 610 citations
• State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation (2011) — 377 citations
• A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving (2020) — 314 citations
• Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions (2018) — 52 citations
• Three Decades of Driver Assistance Systems: Review and Future Perspectives (2014) — 817 citations
• The Labeled Multi-Bernoulli Filter (2014) — 775 citations
• Probabilistic trajectory prediction with Gaussian mixture models (2012) — 337 citations