Pauline Luc

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 6 papers; trusts 593 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 321 citations into it; #16,458 of 21,575 by trust here, #5,082 by citations
• Artificial Intelligence — 262 citations into it; #26,913 of 35,345 by trust here, #10,319 by citations
• Representation & Self-Supervised Learning — 87 citations into it
• Generative Models — 65 citations into it; #1,565 of 1,956 by trust here, #740 by citations
• Model Architectures — 40 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Signal Processing — 37 citations into it
• Optimization & Training — 20 citations into it

Whom Pauline Luc trusts overall

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

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

Who trusts Pauline Luc overall

1. Serban Stan
2. Zizhao Zhang
3. He Huang
4. Sudhanshu Mittal
5. Nasim Souly
6. Apratim Bhattacharyya
7. Wilson Yan
8. Lin F. Yang
9. David Madras
10. Jelmer M. Wolterink

Papers from Pauline Luc's perspective

Ordered by how strongly the researchers Pauline Luc 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Predicting Deeper into the Future of Semantic Segmentation (2017) — 255 citations
• Transformation-based Adversarial Video Prediction on Large-Scale Data (2020) — 95 citations
• Semantic Segmentation using Adversarial Networks (2016) — 756 citations
• Broaden Your Views for Self-Supervised Video Learning (2021) — 146 citations
• Multimodal Self-Supervised Learning of General Audio Representations (2021) — 46 citations
• Flamingo: a Visual Language Model for Few-Shot Learning (2022) — 6,220 citations