Krista A. Ehinger
Computer Vision and Pattern Recognition · 2009–2015
A researcher in the Science corpus: 6 papers; trusts 585 researchers and is trusted by 3,882 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 139 citations into it; #535 of 21,575 by trust here, #1,030 by citations
- Cognitive Neuroscience — 36 citations into it
- Media Technology — 21 citations into it; #2,181 of 3,621 by trust here, #757 by citations
- Artificial Intelligence — 16 citations into it
- Evaluation & Benchmarks — 16 citations into it; #287 of 3,147 by trust here, #261 by citations
- Human-Computer Interaction — 13 citations into it; #91 of 2,253 by trust here, #99 by citations
- Representation & Self-Supervised Learning — 10 citations into it
- Aerospace Engineering — 9 citations into it; #1,453 of 6,421 by trust here, #1,890 by citations
Whom Krista A. Ehinger trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Antonio Torralba
- Aude Oliva
- Derek Hoiem
- Geoffrey E. Hinton
- Navneet Dalal
- Rob Fergus
- Alexei A. Efros
- Alex Krizhevsky
- Kaiming He
- Andrew Zisserman
Who trusts Krista A. Ehinger overall
- Ali Borji
- Antonio Torralba
- Kristen Grauman
- Wenguan Wang
- Bolei Zhou
- Stefan Mathe
- Abhinav Kumar Gupta
- Jiajun Wu
- Jianxiong Xiao
- Yu-Xiong Wang
Papers from Krista A. Ehinger's perspective
Ordered by how strongly the researchers Krista A. Ehinger 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- 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
- SUN database: Large-scale scene recognition from abbey to zoo (2010) — 4,415 citations
- SUN Database: Exploring a Large Collection of Scene Categories (2014) — 509 citations
- Learning to predict where humans look (2009) — 2,189 citations
- Recognizing scene viewpoint using panoramic place representation (2012) — 401 citations
- Modeling Search for People in 900 Scenes: A combined source model of eye guidance (2009) — 330 citations
- TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking (2015) — 425 citations