Francesca Odone

Computer Vision and Pattern Recognition · 2003–2017

A researcher in the Science corpus: 8 papers; trusts 550 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 68 citations into it; #6,810 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 34 citations into it
• Statistical Learning Theory — 19 citations into it
• Statistics and Probability — 15 citations into it
• Mathematical Physics — 12 citations into it; #7,216 of 8,562 by trust here, #4,500 by citations
• Aerospace Engineering — 11 citations into it; #5,315 of 6,421 by trust here, #5,385 by citations
• Computational Mechanics — 11 citations into it; #8,282 of 10,001 by trust here, #6,582 by citations
• Applied Mathematics — 9 citations into it; #6,291 of 7,620 by trust here, #4,033 by citations

Whom Francesca Odone trusts overall

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

1. Ondřej Chum
2. Herv'e J'egou
3. Andrew Zisserman
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Cordelia Schmid
7. David G. Lowe
8. Josef Sivic
9. Florent Perronnin
10. Yuhai Wu

Who trusts Francesca Odone overall

1. Lorenzo Rosasco
2. Vincenzo Lomonaco
3. Kyungjun Lee
4. Hernisa Kacorri
5. Bei Zhao
6. Jason Aaron Edward Weston
7. Radu Bogdan Rusu
8. Ernesto De Vito
9. Luca Baldassarre
10. Andrea Caponnetto

Papers from Francesca Odone's perspective

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

Their papers

• Histogram intersection kernel for image classification (2003) — 354 citations
• One-Shot Learning for Real-Time Action Recognition (2013) — 32 citations
• “Hands On” Visual Recognition for Visually Impaired Users (2017) — 34 citations
• Teaching iCub to recognize objects using deep Convolutional Neural Networks (2015) — 55 citations
• iCub World: Friendly Robots Help Building Good Vision Data-Sets (2013) — 31 citations
• Spectral Algorithms for Supervised Learning (2008) — 172 citations
• Learning from Examples as an Inverse Problem (2005) — 262 citations
• Learning, Regularization and Ill-Posed Inverse Problems (2004) — 17 citations