Michele Volpi

Computer Vision and Pattern Recognition · 2011–2018

A researcher in the Science corpus: 6 papers; trusts 477 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #5,443 of 21,575 by trust here, #4,955 by citations
• Artificial Intelligence — 44 citations into it; #30,258 of 35,345 by trust here, #21,878 by citations
• Media Technology — 40 citations into it; #1,437 of 3,621 by trust here, #809 by citations
• Model Architectures — 30 citations into it; #1,107 of 2,278 by trust here, #1,509 by citations
• Atmospheric Science — 19 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Aerospace Engineering — 11 citations into it
• Ecology — 11 citations into it

Whom Michele Volpi 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. Ilya Sutskever
7. Kaiming He
8. Andrew Zisserman
9. Diederik P. Kingma
10. Pedro F. Felzenszwalb

Who trusts Michele Volpi overall

1. Devis Tuia
2. Diego Marcos
3. Charlotte Pelletier
4. Ying Li
5. Maurice Weiler
6. Xiaoxiang Zhu
7. Xin-Yi Tong
8. Yingchao Feng
9. David W. Romero
10. Daniel E. Worrall

Papers from Michele Volpi's perspective

Ordered by how strongly the researchers Michele Volpi 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Rotation Equivariant Vector Field Networks (2016) — 297 citations
• Learning rotation invariant convolutional filters for texture classification (2016) — 153 citations
• Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models (2018) — 261 citations
• Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks (2016) — 505 citations
• Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014) — 118 citations
• A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification (2011) — 553 citations