Oliver Bringmann

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 456 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 150 citations into it; #19,704 of 21,575 by trust here, #8,409 by citations
• Artificial Intelligence — 129 citations into it
• Safety, Robustness & Interpretability — 62 citations into it
• Evaluation & Benchmarks — 41 citations into it
• Optimization & Training — 19 citations into it
• Model Architectures — 17 citations into it
• Media Technology — 11 citations into it
• Aerospace Engineering — 10 citations into it

Whom Oliver Bringmann trusts overall

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

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

Who trusts Oliver Bringmann overall

1. Shuaicheng Niu

Papers from Oliver Bringmann's perspective

Ordered by how strongly the researchers Oliver Bringmann 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet Large Scale Visual Recognition Challenge
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Improving robustness against common corruptions by covariate shift adaptation (2020) — 651 citations
• A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions (2020) — 243 citations
• Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019) — 574 citations
• Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations (2019) — 91 citations
• Increasing the robustness of DNNs against image corruptions by playing the Game of Noise (2020) — 66 citations
• Simulating Photo-realistic Snow and Fog on Existing Images for Enhanced CNN Training and Evaluation (2019) — 52 citations
• Rendering Physically Correct Raindrops on Windshields for Robustness Verification of Camera-based Object Recognition (2018) — 23 citations