Federica Bogo
Computer Vision and Pattern Recognition · 2014–2021
A researcher in the Science corpus: 7 papers; trusts 739 researchers and is trusted by 1,024 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 256 citations into it; #1,331 of 21,575 by trust here, #2,263 by citations
- Human-Computer Interaction — 64 citations into it
- Control and Systems Engineering — 63 citations into it
- Artificial Intelligence — 47 citations into it
- Computational Mechanics — 46 citations into it; #279 of 10,001 by trust here, #288 by citations
- 3D Vision & Pose — 39 citations into it; #390 of 689 by trust here, #440 by citations
- Aerospace Engineering — 24 citations into it
- Evaluation & Benchmarks — 22 citations into it; #829 of 3,147 by trust here, #1,764 by citations
Whom Federica Bogo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Matthew Maverick Loper
- Michael J. Black
- Alex Krizhevsky
- Diederik P. Kingma
- Ross B. Girshick
- Andrew Zisserman
- Yoshua Bengio
- Yann LeCun
Who trusts Federica Bogo overall
- Michael J. Black
- Georgios Pavlakos
- Davide Boscaini
- Or Litany
- Thiemo Alldieck
- Gerard Pons-Moll
- Angjoo Kanazawa
- Yucheng Chen
- Michael M. Bronstein
- Gyeongsik Moon
Papers from Federica Bogo's perspective
Ordered by how strongly the researchers Federica Bogo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image (2016) — 1,816 citations
- Unite the People: Closing the Loop Between 3D and 2D Human Representations (2017) — 585 citations
- FAUST: Dataset and Evaluation for 3D Mesh Registration (2014) — 831 citations
- H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions (2019) — 287 citations
- Leveraging Photometric Consistency Over Time for Sparsely Supervised Hand-Object Reconstruction (2020) — 226 citations
- Dynamic FAUST: Registering Human Bodies in Motion (2017) — 432 citations
- H2O: Two Hands Manipulating Objects for First Person Interaction Recognition (2021) — 299 citations