Nazli Ikizler-Cinbis

Computer Vision and Pattern Recognition · 2010–2018

A researcher in the Science corpus: 7 papers; trusts 868 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 213 citations into it; #4,811 of 21,575 by trust here, #4,438 by citations
• Artificial Intelligence — 115 citations into it; #18,043 of 35,345 by trust here, #13,163 by citations
• Natural Language Processing — 77 citations into it; #5,039 of 5,688 by trust here, #4,112 by citations
• Multimodal Learning — 74 citations into it; #1,026 of 1,211 by trust here, #685 by citations
• Evaluation & Benchmarks — 52 citations into it; #2,034 of 3,147 by trust here, #2,222 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Biomedical Engineering — 9 citations into it
• Media Technology — 7 citations into it

Whom Nazli Ikizler-Cinbis trusts overall

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

1. Andrew Zisserman
2. Geoffrey E. Hinton
3. David G. Lowe
4. Navneet Dalal
5. Kaiming He
6. Alex Krizhevsky
7. Antonio Torralba
8. Yoshua Bengio
9. Pedro F. Felzenszwalb
10. Ross B. Girshick

Who trusts Nazli Ikizler-Cinbis overall

1. Kishore K. Reddy
2. Xiaodan Liang
3. Alireza Fathi
4. Mubarak Shah
5. Berkan Solmaz
6. Manoj Ramanathan
7. Guo-qing Guo
8. Stan Sclaroff
9. Liang Lin
10. Heng Wang

Papers from Nazli Ikizler-Cinbis's perspective

Ordered by how strongly the researchers Nazli Ikizler-Cinbis 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Object, Scene and Actions: Combining Multiple Features for Human Action Recognition (2010) — 318 citations
• Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (2016) — 393 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
• On Recognizing Actions in Still Images via Multiple Features (2012) — 36 citations
• Web-Based Classifiers for Human Action Recognition (2012) — 29 citations