Fabio Cermelli

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 933 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 216 citations into it; #7,730 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 177 citations into it; #11,400 of 35,345 by trust here, #9,465 by citations
• Meta & Continual Learning — 67 citations into it; #407 of 1,041 by trust here, #331 by citations
• Representation & Self-Supervised Learning — 34 citations into it
• Optimization & Training — 24 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 20 citations into it
• Radiology, Nuclear Medicine and Imaging — 16 citations into it

Whom Fabio Cermelli trusts overall

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

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

Who trusts Fabio Cermelli overall

1. Arthur Douillard
2. Beomyoung Kim
3. Da-Wei Zhou
4. Umberto Michieli
5. Minh-Hieu Phan
6. Tyler L. Hayes
7. Matthieu Cord
8. Yifu Chen
9. Liyuan Wang
10. Mathieu Pagé Fortin

Papers from Fabio Cermelli's perspective

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

Their papers

• Modeling the Background for Incremental Learning in Semantic Segmentation (2020) — 373 citations
• CoMFormer: Continual Learning in Semantic and Panoptic Segmentation (2022) — 49 citations
• Incremental Learning in Semantic Segmentation from Image Labels (2021) — 69 citations
• Prototype-based Incremental Few-Shot Segmentation (2020) — 44 citations
• A Few Guidelines for Incremental Few-Shot Segmentation (2020) — 10 citations