Pietro Morerio

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 638 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #21,561 of 21,575 by trust here, #13,269 by citations
• Artificial Intelligence — 60 citations into it
• Biomedical Engineering — 23 citations into it
• Signal Processing — 18 citations into it
• Multimodal Learning — 17 citations into it
• Optimization & Training — 16 citations into it
• Human-Computer Interaction — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Pietro Morerio trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Andrew Zisserman
9. Karen Simonyan
10. Nitish Srivastava

Who trusts Pietro Morerio overall

1. Minjia Zhang
2. Xin Wang
3. Zeyi Huang
4. Petru Soviany
5. Jianping Gou
6. Canwen Xu
7. Yan Lu
8. Yuxiong He
9. Andrés F. Pérez
10. Yudong Chen

Papers from Pietro Morerio's perspective

Ordered by how strongly the researchers Pietro Morerio 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Modality Distillation with Multiple Stream Networks for Action Recognition (2018) — 214 citations
• Curriculum Dropout (2017) — 121 citations
• When Kernel Methods Meet Feature Learning: Log-Covariance Network for Action Recognition From Skeletal Data (2017) — 10 citations
• Audio-Visual Model Distillation Using Acoustic Images (2019) — 31 citations