Sinan Kalkan

Computer Vision and Pattern Recognition · 2013–2020

A researcher in the Science corpus: 5 papers; trusts 1,100 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 204 citations into it; #15,584 of 21,575 by trust here, #9,305 by citations
• Artificial Intelligence — 138 citations into it
• Cognitive Neuroscience — 27 citations into it
• Model Architectures — 22 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Aerospace Engineering — 12 citations into it; #4,195 of 6,421 by trust here, #5,830 by citations

Whom Sinan Kalkan trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. David G. Lowe
6. Andrew Zisserman
7. Yann LeCun
8. Yoshua Bengio
9. Shaoqing Ren
10. Diederik P. Kingma

Who trusts Sinan Kalkan overall

1. Bilal Taha
2. Yu Huang
3. Abdulhadi Shoufan

Papers from Sinan Kalkan's perspective

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

Their papers

• Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? (2013) — 389 citations
• Imbalance Problems in Object Detection: A Review (2019) — 568 citations
• Continual Learning for Affective Robotics: Why, What and How? (2020) — 56 citations
• Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition (2020) — 162 citations
• Using deep networks for drone detection (2017) — 201 citations