Lluís Castrejón

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 1,060 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 219 citations into it; #3,783 of 21,575 by trust here, #5,053 by citations
• Artificial Intelligence — 153 citations into it
• Representation & Self-Supervised Learning — 122 citations into it; #2,906 of 3,145 by trust here, #1,781 by citations
• Generative Models — 25 citations into it; #1,166 of 1,956 by trust here, #1,601 by citations
• Optimization & Training — 24 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Signal Processing — 14 citations into it
• Media Technology — 10 citations into it

Whom Lluís Castrejón trusts overall

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

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

Who trusts Lluís Castrejón overall

1. Yue Wang
2. J. Liew
3. Gabriella Csurka
4. Huan Ling
5. Fisher Yu
6. Andreas Blattmann
7. Jasper R. R. Uijlings
8. Vincent Le Guen
9. David Acuna
10. Sanja Fidler

Papers from Lluís Castrejón's perspective

Ordered by how strongly the researchers Lluís Castrejón 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. 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

• Annotating Object Instances with a Polygon-RNN (2017) — 390 citations
• Learning Aligned Cross-Modal Representations from Weakly Aligned Data (2016) — 183 citations
• Cross-Modal Scene Networks (2016) — 119 citations
• Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations (2020) — 3 citations
• Improved Conditional VRNNs for Video Prediction (2019) — 180 citations
• Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations. (2020) — 18 citations