Aykut Erdem

Computer Vision and Pattern Recognition · 2013–2018

A researcher in the Science corpus: 9 papers; trusts 921 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 336 citations into it; #5,648 of 21,575 by trust here, #3,280 by citations
• Artificial Intelligence — 122 citations into it; #18,044 of 35,345 by trust here, #13,163 by citations
• Multimodal Learning — 78 citations into it; #1,027 of 1,211 by trust here, #685 by citations
• Natural Language Processing — 78 citations into it; #5,040 of 5,688 by trust here, #4,112 by citations
• Generative Models — 64 citations into it; #1,525 of 1,956 by trust here, #1,287 by citations
• Evaluation & Benchmarks — 62 citations into it; #2,035 of 3,147 by trust here, #2,222 by citations
• Media Technology — 15 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Aykut Erdem trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Antonio Torralba
9. Karen Simonyan
10. Ross B. Girshick

Who trusts Aykut Erdem overall

1. Asli Celikyilmaz
2. Elizabeth Clark
3. Ali Borji
4. Matteo Stefanini
5. E. Vig
6. Xun Huang
7. Li Luo
8. Xiangyang Li
9. Taichi Iki
10. Jun-Yan Zhu

Papers from Aykut Erdem's perspective

Ordered by how strongly the researchers Aykut Erdem trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• The Visual Object Tracking VOT2016 Challenge Results (2016) — 773 citations
• Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts (2016) — 192 citations
• Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (2016) — 393 citations
• Manipulating Attributes of Natural Scenes via Hallucination (2018) — 24 citations
• Re-evaluating Automatic Metrics for Image Captioning (2016) — 194 citations
• RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes (2018) — 196 citations
• Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (Extended Abstract) (2017) — 25 citations
• Visual saliency estimation by nonlinearly integrating features using region covariances. (2013) — 416 citations
• Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks (2018) — 581 citations