Lina Karam
Computer Vision and Pattern Recognition · 2009–2019
A researcher in the Science corpus: 8 papers; trusts 653 researchers and is trusted by 56 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 91 citations into it; #3,437 of 21,575 by trust here, #5,082 by citations
- Artificial Intelligence — 36 citations into it
- Media Technology — 25 citations into it; #212 of 3,621 by trust here, #417 by citations
- Evaluation & Benchmarks — 14 citations into it
- Model Architectures — 12 citations into it
- Safety, Robustness & Interpretability — 12 citations into it; #784 of 2,251 by trust here, #1,329 by citations
- Cognitive Neuroscience — 10 citations into it
- Optimization & Training — 9 citations into it
Whom Lina Karam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karen Simonyan
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Christian Szegedy
- Andrew Zisserman
- Ross B. Girshick
- Ian J. Goodfellow
- Yoshua Bengio
- Ilya Sutskever
Who trusts Lina Karam overall
- Samuel F. Dodge
- Ke Gu
- Damon M. Chandler
- Prasun Roy
- Raphael Gontijo Lopes
- Evgenia Rusak
- Guangtao Zhai
- Robert Geirhos
- Anush K. Moorthy
- Xiaokang Yang
Papers from Lina Karam's perspective
Ordered by how strongly the researchers Lina Karam trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions (2017) — 464 citations
- Understanding how image quality affects deep neural networks (2016) — 815 citations
- Human and DNN Classification Performance on Images With Quality Distortions (2019) — 40 citations
- Quality Resilient Deep Neural Networks (2017) — 47 citations
- A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) (2009) — 794 citations
- A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD) (2011) — 600 citations
- Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments (2015) — 54 citations
- Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison (2011) — 645 citations