Matteo Stefanini

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 1,002 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 300 citations into it; #9,577 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 199 citations into it; #14,881 of 35,345 by trust here, #15,004 by citations
• Multimodal Learning — 184 citations into it; #518 of 1,211 by trust here, #770 by citations
• Natural Language Processing — 90 citations into it
• Evaluation & Benchmarks — 44 citations into it
• Natural Language Generation — 41 citations into it
• Model Architectures — 32 citations into it
• Large Language Models — 10 citations into it

Whom Matteo Stefanini trusts overall

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

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

Who trusts Matteo Stefanini overall

1. Marcella Cornia
2. Ron Mokady
3. Yuhao Zhang
4. Gianni Brauwers
5. Viktar Atliha
6. Lorenzo Baraldi
7. Yasuhide Miura
8. Amir Hertz
9. Xu Yang
10. Daoquan Chen

Papers from Matteo Stefanini's perspective

Ordered by how strongly the researchers Matteo Stefanini trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Meshed-Memory Transformer for Image Captioning (2019) — 1,111 citations
• From Show to Tell: A Survey on Deep Learning-Based Image Captioning (2021) — 395 citations
• From Show to Tell: A Survey on Image Captioning (2021) — 58 citations