Silvano Galliani

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 664 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #5,271 of 21,575 by trust here, #3,848 by citations
• Aerospace Engineering — 37 citations into it; #6,333 of 6,421 by trust here, #1,183 by citations
• Media Technology — 34 citations into it
• Artificial Intelligence — 29 citations into it
• 3D Vision & Pose — 25 citations into it
• Computer Graphics and Computer-Aided Design — 21 citations into it
• Model Architectures — 18 citations into it
• Geology — 16 citations into it

Whom Silvano Galliani trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Robert T. Collins
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Kaiming He
7. David G. Lowe
8. Yoshua Bengio
9. Yasutaka Furukawa
10. Ross B. Girshick

Who trusts Silvano Galliani overall

1. Yao Yao
2. Mengqi Ji
3. Johannes L. Schönberger
4. Zixin Luo
5. Shuo Cheng
6. Fangjinhua Wang
7. Christian Bailer
8. Konrad Schindler
9. Thomas Schöps
10. Juergen Gall

Papers from Silvano Galliani's perspective

Ordered by how strongly the researchers Silvano Galliani 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: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos (2017) — 1,187 citations
• Massively Parallel Multiview Stereopsis by Surface Normal Diffusion (2015) — 657 citations
• Learned Multi-patch Similarity (2017) — 113 citations
• Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction (2016) — 25 citations
• 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
• PatchmatchNet: Learned Multi-View Patchmatch Stereo (2020) — 460 citations
• Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018) — 342 citations