Biagio Brattoli

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 794 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 113 citations into it; #9,617 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 77 citations into it; #14,959 of 35,345 by trust here, #13,962 by citations
• Representation & Self-Supervised Learning — 30 citations into it; #1,611 of 3,145 by trust here, #2,094 by citations
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Optimization & Training — 10 citations into it
• Generative Models — 6 citations into it
• Reinforcement Learning — 5 citations into it

Whom Biagio Brattoli trusts overall

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

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

Who trusts Biagio Brattoli overall

1. Longlong Jing
2. Yingli Tian
3. Yu Kong
4. Jiangliu Wang
5. Michael Tschannen
6. Dejing Xu

Papers from Biagio Brattoli's perspective

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

Their papers

• VidTr: Video Transformer Without Convolutions (2021) — 226 citations
• Cross and Learn: Cross-Modal Self-Supervision (2018) — 86 citations
• Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning (2018) — 121 citations