Alessandro Bergamo

Computer Vision and Pattern Recognition · 2010–2014

A researcher in the Science corpus: 5 papers; trusts 1,009 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #6,022 of 21,575 by trust here, #9,722 by citations
• Artificial Intelligence — 40 citations into it; #14,538 of 35,345 by trust here, #17,110 by citations
• Evaluation & Benchmarks — 16 citations into it
• Aerospace Engineering — 15 citations into it; #2,469 of 6,421 by trust here, #5,172 by citations
• Media Technology — 13 citations into it; #1,653 of 3,621 by trust here, #3,091 by citations
• Representation & Self-Supervised Learning — 11 citations into it; #614 of 3,145 by trust here, #1,561 by citations
• Natural Language Processing — 9 citations into it
• Signal Processing — 5 citations into it

Whom Alessandro Bergamo trusts overall

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

1. Andrew Zisserman
2. David G. Lowe
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Jia Deng
6. Kaiming He
7. Rob Fergus
8. Ross B. Girshick
9. Yoshua Bengio
10. Yann LeCun

Who trusts Alessandro Bergamo overall

1. Tobias Weyand
2. Jingjing Zheng
3. Gabriella Csurka
4. Judy Hoffman
5. Boqing Gong
6. Artem Babenko
7. Trevor Darrell
8. Saeid Motiian
9. Kristen Grauman
10. Xinlei Chen

Papers from Alessandro Bergamo's perspective

Ordered by how strongly the researchers Alessandro Bergamo 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Self-taught object localization with deep networks (2014) — 160 citations
• Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification (2013) — 54 citations
• AutoCaption: Automatic caption generation for personal photos (2014) — 32 citations
• Meta-class features for large-scale object categorization on a budget (2012) — 65 citations
• Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach (2010) — 261 citations