Fabien Baradel

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 867 researchers and is trusted by 506 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 265 citations into it; #1,878 of 21,575 by trust here, #3,426 by citations
• Artificial Intelligence — 166 citations into it; #25,614 of 35,345 by trust here, #20,836 by citations
• Model Architectures — 53 citations into it
• Biomedical Engineering — 49 citations into it
• Representation & Self-Supervised Learning — 49 citations into it; #924 of 3,145 by trust here, #825 by citations
• Multimodal Learning — 36 citations into it
• Human-Computer Interaction — 33 citations into it; #1,080 of 2,253 by trust here, #1,515 by citations
• Evaluation & Benchmarks — 29 citations into it

Whom Fabien Baradel trusts overall

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

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

Who trusts Fabien Baradel overall

1. Jun Liu
2. Diogo Carbonera Luvizon
3. Zehua Sun
4. Rohit Girdhar
5. Linjie Li
6. Jack Hessel
7. Arsha Nagrani
8. Christoph Feichtenhofer
9. Yen-Chun Chen
10. Bugra Tekin

Papers from Fabien Baradel's perspective

Ordered by how strongly the researchers Fabien Baradel trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Contrastive Bidirectional Transformer for Temporal Representation Learning (2019) — 137 citations
• Learning Video Representations using Contrastive Bidirectional Transformer (2019) — 283 citations
• Object Level Visual Reasoning in Videos (2018) — 173 citations
• COPHY: Counterfactual Learning of Physical Dynamics (2019) — 114 citations
• Human Action Recognition: Pose-Based Attention Draws Focus to Hands (2017) — 122 citations
• Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points (2018) — 177 citations
• Pose-conditioned Spatio-Temporal Attention for Human Action Recognition (2017) — 83 citations