Dim P. Papadopoulos
Computer Vision and Pattern Recognition · 2014–2017
A researcher in the Science corpus: 4 papers; trusts 1,248 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 219 citations into it; #5,705 of 21,575 by trust here, #7,750 by citations
- Artificial Intelligence — 69 citations into it; #24,113 of 35,345 by trust here, #27,551 by citations
- Evaluation & Benchmarks — 37 citations into it; #1,557 of 3,147 by trust here, #2,627 by citations
- Aerospace Engineering — 18 citations into it
- Media Technology — 18 citations into it
- Representation & Self-Supervised Learning — 18 citations into it
- Human-Computer Interaction — 13 citations into it; #1,282 of 2,253 by trust here, #763 by citations
- Cognitive Science & Psychology — 10 citations into it
Whom Dim P. Papadopoulos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Alex Krizhevsky
- Karen Simonyan
- Geoffrey E. Hinton
- Kaiming He
- Andrew Zisserman
- Jia Deng
- Ilya Sutskever
- David G. Lowe
- Yoshua Bengio
Who trusts Dim P. Papadopoulos overall
- Vittorio Ferrari
- Jasper R. R. Uijlings
- Rodrigo Benenson
- Michael Gygli
- Eirikur Agustsson
- Qizhu Li
- Stefan Popov
- Bowen Cheng
- Xingyi Zhou
- Yusuke Sugano
Papers from Dim P. Papadopoulos's perspective
Ordered by how strongly the researchers Dim P. Papadopoulos 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
- Gradient-based learning applied to document recognition
- 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
- Extreme Clicking for Efficient Object Annotation (2017) — 259 citations
- Training Object Class Detectors from Eye Tracking Data (2014) — 153 citations
- We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification (2016) — 144 citations
- Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017)