Linda G. Shapiro

Computer Vision and Pattern Recognition · 2001–2018

A researcher in the Science corpus: 7 papers; trusts 768 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 143 citations into it; #5,263 of 21,575 by trust here, #5,161 by citations
• Artificial Intelligence — 74 citations into it; #21,123 of 35,345 by trust here, #11,192 by citations
• Efficiency & Compression — 31 citations into it
• Model Architectures — 30 citations into it
• Optimization & Training — 17 citations into it
• Aerospace Engineering — 16 citations into it; #3,263 of 6,421 by trust here, #4,945 by citations
• Evaluation & Benchmarks — 16 citations into it
• Biophysics — 6 citations into it

Whom Linda G. Shapiro trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. David G. Lowe
5. Diederik P. Kingma
6. Christian Szegedy
7. Ilya Sutskever
8. Ross B. Girshick
9. Yoshua Bengio
10. Yann LeCun

Who trusts Linda G. Shapiro overall

1. Edward Rosten
2. Sachin Mehta
3. Jing Yuan
4. Volker Blanz
5. Minjoon Seo
6. Clemens V. Verhoosel
7. Haibin Ling
8. Andreas Baak
9. Tat-seng Chua
10. Pan Lu

Papers from Linda G. Shapiro's perspective

Ordered by how strongly the researchers Linda G. Shapiro 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Unsupervised Template Learning for Fine-Grained Object Recognition (2012) — 167 citations
• ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation (2018) — 898 citations
• ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network (2018) — 473 citations
• A SIFT descriptor with global context (2005) — 300 citations
• Modeling Stylized Character Expressions via Deep Learning (2016) — 148 citations
• Computer and Robot Vision (Volume II) (2002) — 344 citations
• Computer Vision (2001) — 1,677 citations