Uwe Stilla

Computer Vision and Pattern Recognition · 2003–2020

A researcher in the Science corpus: 9 papers; trusts 239 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 78 citations into it; #10,854 of 21,575 by trust here, #5,987 by citations
• Artificial Intelligence — 24 citations into it; #22,335 of 35,345 by trust here, #9,968 by citations
• Environmental Engineering — 24 citations into it; #664 of 5,479 by trust here, #915 by citations
• Media Technology — 16 citations into it; #2,116 of 3,621 by trust here, #956 by citations
• Computational Mechanics — 13 citations into it
• Insect Science — 9 citations into it; #262 of 2,259 by trust here, #641 by citations
• Model Architectures — 9 citations into it
• Nature and Landscape Conservation — 9 citations into it; #1,358 of 6,941 by trust here, #2,544 by citations

Whom Uwe Stilla trusts overall

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

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

Who trusts Uwe Stilla overall

1. Francesco Pirotti
2. P. Dorninger
3. Wei Yao
4. Norbert Pfeifer
5. J. Reitberger
6. Peter Krzystek
7. Fabio Remondino
8. Xiaowei Yu
9. Jixian Zhang
10. Chaopeng Shen

Papers from Uwe Stilla's perspective

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

Their papers

• Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection (2016) — 665 citations
• SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS (2016) — 334 citations
• Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark (2020) — 383 citations
• Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks (2016) — 581 citations
• 3D segmentation of single trees exploiting full waveform LIDAR data (2009) — 489 citations
• Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees (2008) — 324 citations
• Topographic Laser Ranging and Scanning: Principles and Processing (2008) — 306 citations
• Range determination with waveform recording laser systems using a Wiener Filter (2006) — 227 citations
• Potential and limits of InSAR data for building reconstruction in built-up areas (2003) — 128 citations