Holger Caesar
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 6 papers; trusts 1,131 researchers and is trusted by 1,361 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 236 citations into it; #1,124 of 21,575 by trust here, #1,368 by citations
- Artificial Intelligence — 58 citations into it; #22,130 of 35,345 by trust here, #25,809 by citations
- Aerospace Engineering — 49 citations into it; #3,156 of 6,421 by trust here, #641 by citations
- Evaluation & Benchmarks — 39 citations into it; #275 of 3,147 by trust here, #384 by citations
- Automotive Engineering — 28 citations into it; #2,749 of 3,081 by trust here, #405 by citations
- Model Architectures — 21 citations into it
- Environmental Engineering — 15 citations into it
- Media Technology — 11 citations into it
Whom Holger Caesar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Andrew Zisserman
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Holger Caesar overall
- Yuhui Yuan
- Xiaodan Liang
- Tianfu Wu
- Liang-Chieh Chen
- Robin Rombach
- Chong Zhou
- Arthur Douillard
- John Lambert
- Taesung Park
- Mengde Xu
Papers from Holger Caesar's perspective
Ordered by how strongly the researchers Holger Caesar trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- COCO-Stuff: Thing and Stuff Classes in Context (2016) — 1,715 citations
- nuScenes: A Multimodal Dataset for Autonomous Driving (2019) — 8,536 citations
- PointPillars: Fast Encoders for Object Detection From Point Clouds (2018) — 4,614 citations
- Weakly Supervised Object Localization Using Things and Stuff Transfer (2017) — 75 citations
- Region-Based Semantic Segmentation with End-to-End Training (2016) — 87 citations
- nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles (2021) — 620 citations