Marco Toldo

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 796 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #8,675 of 21,575 by trust here, #13,542 by citations
• Artificial Intelligence — 101 citations into it; #13,273 of 35,345 by trust here, #20,377 by citations
• Representation & Self-Supervised Learning — 38 citations into it; #2,120 of 3,145 by trust here, #3,089 by citations
• Meta & Continual Learning — 32 citations into it; #459 of 1,041 by trust here, #756 by citations
• Radiology, Nuclear Medicine and Imaging — 15 citations into it; #2,038 of 3,265 by trust here, #2,195 by citations
• Evaluation & Benchmarks — 12 citations into it
• Generative Models — 9 citations into it
• Model Architectures — 9 citations into it

Whom Marco Toldo trusts overall

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

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

Who trusts Marco Toldo overall

1. Liyuan Wang
2. Umberto Michieli
3. Simone Magistri
4. Xingxing Zhang
5. Pietro Zanuttigh
6. Fabio Cermelli
7. Tomaso Trinci
8. Hang Su
9. Albin Soutif-Cormerais
10. Mathieu Pagé Fortin

Papers from Marco Toldo's perspective

Ordered by how strongly the researchers Marco Toldo 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. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• RECALL: Replay-based Continual Learning in Semantic Segmentation (2021) — 157 citations
• Bring Evanescent Representations to Life in Lifelong Class Incremental Learning (2022) — 51 citations
• Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings (2020) — 48 citations