Dogancan Temel
Computer Vision and Pattern Recognition · 2017–2018
A researcher in the Science corpus: 3 papers; trusts 170 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 38 citations into it; #15,647 of 21,575 by trust here, #17,757 by citations
- Artificial Intelligence — 12 citations into it
- Evaluation & Benchmarks — 10 citations into it; #2,265 of 3,147 by trust here, #2,853 by citations
- Media Technology — 10 citations into it
Whom Dogancan Temel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David G. Lowe
- Karen Simonyan
- Navneet Dalal
- Andrew Zisserman
- Christian Szegedy
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Jia Deng
- Yann LeCun
Who trusts Dogancan Temel overall
Papers from Dogancan Temel's perspective
Ordered by how strongly the researchers Dogancan Temel 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)
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- CURE-OR: Challenging Unreal and Real Environments for Object Recognition (2018) — 45 citations
- Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions] (2018) — 28 citations
- CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition (2017) — 81 citations