Volker Steinhage

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 8 papers; trusts 589 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #10,251 of 21,575 by trust here, #12,419 by citations
• Aerospace Engineering — 28 citations into it; #2,450 of 6,421 by trust here, #4,066 by citations
• Artificial Intelligence — 27 citations into it
• Ecology — 25 citations into it; #9,136 of 10,717 by trust here, #9,634 by citations
• Model Architectures — 20 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Ecological Modeling — 13 citations into it; #2,274 of 2,530 by trust here, #2,330 by citations
• Media Technology — 10 citations into it

Whom Volker Steinhage trusts overall

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

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

Who trusts Volker Steinhage overall

1. Timm Haucke
2. Bo Li
3. Jens Behley
4. Peter Johanns
5. Daniel Maturana
6. Mohsen Yavartanoo
7. Hjalmar S. Kühl
8. Tongtong Chen
9. Alejandro González
10. Alireza Asvadi

Papers from Volker Steinhage's perspective

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

Their papers

• Laser-based segment classification using a mixture of bag-of-words (2013) — 100 citations
• SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision (2022) — 13 citations
• Exploiting Depth Information for Wildlife Monitoring (2021) — 16 citations
• Automated distance estimation for wildlife camera trapping (2022) — 26 citations
• Wildlife 3D multi-object tracking (2022) — 11 citations
• Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps (2021) — 28 citations
• Improving wildlife tracking using 3D information (2021) — 13 citations
• Performance of histogram descriptors for the classification of 3D laser range data in urban environments (2012) — 111 citations