Tim Fingscheidt

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 405 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it
• Artificial Intelligence — 46 citations into it
• Signal Processing — 16 citations into it; #5,883 of 7,572 by trust here, #6,591 by citations
• Media Technology — 15 citations into it
• Aerospace Engineering — 11 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 9 citations into it
• Speech & Audio — 9 citations into it

Whom Tim Fingscheidt trusts overall

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

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

Who trusts Tim Fingscheidt overall

1. Haici Yang
2. Wootaek Lim
3. Minje Kim

Papers from Tim Fingscheidt's perspective

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

Their papers

• Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance (2020) — 396 citations
• Convolutional Neural Networks to Enhance Coded Speech (2018) — 75 citations
• Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels (2020) — 48 citations
• Artificial bandwidth extension using deep neural networks for spectral envelope estimation (2016) — 26 citations