Pietro Zanuttigh

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 8 papers; trusts 1,590 researchers and is trusted by 100 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 281 citations into it; #2,966 of 21,575 by trust here, #4,623 by citations
• Artificial Intelligence — 236 citations into it; #2,221 of 35,345 by trust here, #6,422 by citations
• Representation & Self-Supervised Learning — 78 citations into it
• Meta & Continual Learning — 46 citations into it; #37 of 1,041 by trust here, #194 by citations
• Evaluation & Benchmarks — 37 citations into it
• Model Architectures — 25 citations into it
• Radiology, Nuclear Medicine and Imaging — 25 citations into it; #2,035 of 3,265 by trust here, #2,195 by citations
• Generative Models — 20 citations into it

Whom Pietro Zanuttigh trusts overall

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

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

Who trusts Pietro Zanuttigh overall

1. Umberto Michieli
2. Fabio Cermelli
3. Arthur Douillard
4. Andrea Maracani
5. Guanglei Yang
6. Zekang Zhang
7. Mathieu Pagé Fortin
8. Da-Wei Zhou
9. Marvin Klingner
10. Beomyoung Kim

Papers from Pietro Zanuttigh's perspective

Ordered by how strongly the researchers Pietro Zanuttigh 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

• Incremental Learning Techniques for Semantic Segmentation (2019) — 289 citations
• RECALL: Replay-based Continual Learning in Semantic Segmentation (2021) — 157 citations
• Knowledge Distillation for Incremental Learning in Semantic Segmentation (2019) — 109 citations
• Hand gesture recognition with leap motion and kinect devices (2014) — 432 citations
• Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (2021) — 176 citations
• Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings (2020) — 48 citations
• Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes (2019) — 52 citations
• Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation (2019) — 65 citations