Amaia Salvador

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 843 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 161 citations into it; #16,863 of 21,575 by trust here, #12,689 by citations
• Artificial Intelligence — 115 citations into it
• Model Architectures — 37 citations into it
• Natural Language Processing — 36 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Multimodal Learning — 21 citations into it; #913 of 1,211 by trust here, #984 by citations
• Biomedical Engineering — 13 citations into it

Whom Amaia Salvador trusts overall

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

1. Kaiming He
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Oriol Vinyals
7. Karen Simonyan
8. Ross B. Girshick
9. Yoshua Bengio
10. Shaoqing Ren

Who trusts Amaia Salvador overall

1. Luis Villaseñor Pineda
2. Fadime Sener
3. Michal Drozdzal
4. Peiqin Zhuang
5. Weiqing Min
6. Hao Wang
7. Semih Yagcioglu
8. Adriana Romero
9. Angela Yao
10. Yali Wang

Papers from Amaia Salvador's perspective

Ordered by how strongly the researchers Amaia Salvador 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

• Learning Cross-Modal Embeddings for Cooking Recipes and Food Images (2017) — 316 citations
• Recurrent Neural Networks for Semantic Instance Segmentation (2017) — 65 citations
• Inverse Cooking: Recipe Generation From Food Images (2018) — 170 citations
• Recipe1M: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images (2018) — 386 citations
• Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019) — 15 citations