Pedro Morgado

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 1,617 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 222 citations into it; #9,963 of 21,575 by trust here, #8,831 by citations
• Artificial Intelligence — 163 citations into it; #31,771 of 35,345 by trust here, #29,412 by citations
• Representation & Self-Supervised Learning — 130 citations into it; #2,380 of 3,145 by trust here, #2,094 by citations
• Signal Processing — 40 citations into it; #3,482 of 7,572 by trust here, #2,184 by citations
• Evaluation & Benchmarks — 23 citations into it
• Optimization & Training — 22 citations into it
• Model Architectures — 20 citations into it
• Multimodal Learning — 20 citations into it; #810 of 1,211 by trust here, #892 by citations

Whom Pedro Morgado trusts overall

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

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

Who trusts Pedro Morgado overall

1. Senthil Purushwalkam
2. Ruohan Gao
3. Kristen Grauman
4. Zaiwei Zhang
5. Xu Jia
6. Rohit Girdhar
7. Han Zhang
8. Jatin Lamba
9. Jean-Baptiste Alayrac
10. Yonglong Tian

Papers from Pedro Morgado's perspective

Ordered by how strongly the researchers Pedro Morgado 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

• Audio-Visual Instance Discrimination with Cross-Modal Agreement (2020) — 314 citations
• Self-Supervised Generation of Spatial Audio for 360 Video (2018) — 200 citations
• The Challenges of Continuous Self-Supervised Learning (2022) — 59 citations
• NetTailor: Tuning the Architecture, Not Just the Weights (2019) — 29 citations
• Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier (2020) — 59 citations