Giuseppe Amato

Computer Vision and Pattern Recognition · 2005–2021

A researcher in the Science corpus: 10 papers; trusts 641 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 164 citations into it; #11,919 of 21,575 by trust here, #11,950 by citations
• Artificial Intelligence — 79 citations into it
• Multimodal Learning — 26 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #2,699 of 3,145 by trust here, #2,863 by citations
• Natural Language Processing — 11 citations into it
• Media Technology — 9 citations into it
• Model Architectures — 8 citations into it

Whom Giuseppe Amato trusts overall

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

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

Who trusts Giuseppe Amato overall

1. Luca Ciampi
2. Carlos Santiago
3. Claudio Gennaro
4. Sung-Hyuk Cha
5. João Paulo Costeira
6. M. Mohammadi
7. Fotios Zantalis
8. Fabrizio Falchi
9. Leonid Boytsov

Papers from Giuseppe Amato's perspective

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

Their papers

• Domain Adaptation for Traffic Density Estimation (2021) — 31 citations
• Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using Transformer Encoders (2020) — 169 citations
• Deep learning for decentralized parking lot occupancy detection (2017) — 341 citations
• A Wireless Smart Camera Network for Parking Monitoring (2018) — 25 citations
• Unsupervised vehicle counting via multiple camera domain adaptation (2020) — 10 citations
• Virtual to Real Adaptation of Pedestrian Detectors (2020) — 38 citations
• Learning Pedestrian Detection from Virtual Worlds (2019) — 29 citations
• Counting Vehicles with Deep Learning in Onboard UAV Imagery (2019) — 47 citations
• Counting Vehicles with Cameras (2018) — 23 citations
• Similarity Search: The Metric Space Approach (Advances in Database Systems) (2005) — 979 citations