Pablo Garrido
Computer Vision and Pattern Recognition · 2013–2018
A researcher in the Science corpus: 6 papers; trusts 291 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 196 citations into it; #3,986 of 21,575 by trust here, #2,949 by citations
- Computational Mechanics — 41 citations into it; #5,392 of 10,001 by trust here, #2,203 by citations
- Computer Graphics and Computer-Aided Design — 29 citations into it
- Signal Processing — 25 citations into it
- Control and Systems Engineering — 23 citations into it
- Artificial Intelligence — 6 citations into it
- Neurology — 5 citations into it
Whom Pablo Garrido trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Volker Blanz
- Shahram Izadi
- Michael Zollhöfer
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Thomas Vetter
- Thabo Beeler
- Supasorn Suwajanakorn
- Ira Kemelmacher-Shlizerman
Who trusts Pablo Garrido overall
- Michael Zollhöfer
- Ayush Kumar Tewari
- Justus Thies
- Hyeongwoo Kim
- Christian Theobalt
- Qianru Sun
- Timo Bolkart
- Luan Tran
- Baris Gecer
- Matthias Nießner
Papers from Pablo Garrido's perspective
Ordered by how strongly the researchers Pablo Garrido trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- GENERATIVE ADVERSARIAL NETS
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Deep video portraits (2018) — 780 citations
- MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction (2017) — 585 citations
- Reconstruction of Personalized 3D Face Rigs from Monocular Video (2016) — 280 citations
- State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018) — 344 citations
- Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz (2017) — 277 citations
- Reconstructing detailed dynamic face geometry from monocular video (2013) — 225 citations