Concetto Spampinato

Computer Vision and Pattern Recognition · 2013–2022

A researcher in the Science corpus: 10 papers; trusts 701 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 221 citations into it; #10,107 of 21,575 by trust here, #8,502 by citations
• Artificial Intelligence — 184 citations into it; #17,924 of 35,345 by trust here, #16,166 by citations
• Meta & Continual Learning — 72 citations into it
• Representation & Self-Supervised Learning — 63 citations into it
• Optimization & Training — 29 citations into it
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Reinforcement Learning — 14 citations into it

Whom Concetto Spampinato trusts overall

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

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

Who trusts Concetto Spampinato overall

1. Pranay Mukherjee
2. Lihe Yang
3. Changying Du
4. Xiaotong Gu
5. Yassine Ouali
6. Zhengwei Wang
7. Sudhanshu Mittal
8. Thierry Bouwmans
9. Yanbei Chen
10. Xiaokang Chen

Papers from Concetto Spampinato's perspective

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

Their papers

• Semi Supervised Semantic Segmentation Using Generative Adversarial Network (2017) — 473 citations
• KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection (2017) — 875 citations
• Deep Learning Human Mind for Automated Visual Classification (2016) — 314 citations
• On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (2022) — 63 citations
• Transfer without Forgetting (2022) — 74 citations
• Effects of Auxiliary Knowledge on Continual Learning (2022) — 9 citations
• MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning (2020) — 25 citations
• Deep learning for automated skeletal bone age assessment in X‐ray images (2017) — 394 citations
• Nonparametric label propagation using mutual local similarity in nearest neighbors (2015) — 7 citations
• An innovative web-based collaborative platform for video annotation (2013) — 106 citations