Caner Hazirbas

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 5 papers; trusts 518 researchers and is trusted by 526 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 136 citations into it; #1,865 of 21,575 by trust here, #1,634 by citations
• Aerospace Engineering — 34 citations into it; #4,286 of 6,421 by trust here, #1,628 by citations
• Artificial Intelligence — 30 citations into it
• Model Architectures — 21 citations into it
• Media Technology — 14 citations into it
• Optimization & Training — 11 citations into it
• Computational Mechanics — 8 citations into it; #5,462 of 10,001 by trust here, #4,749 by citations

Whom Caner Hazirbas trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Karen Simonyan
4. Ross B. Girshick
5. Kaiming He
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. L. Eon Bottou
10. Andrew Zisserman

Who trusts Caner Hazirbas overall

1. Yuxiang Sun
2. H. Wang
3. Seungyong Lee
4. Ming Liu
5. Hengli Wang
6. Alexandre Boulch
7. Alberto Garcia-Garcia
8. Yang Hu
9. Xianping Ma
10. Kai Zhang

Papers from Caner Hazirbas's perspective

Ordered by how strongly the researchers Caner Hazirbas 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
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

• FlowNet: Learning Optical Flow with Convolutional Networks (2015) — 4,664 citations
• Image-Based Localization Using LSTMs for Structured Feature Correlation (2016) — 545 citations
• Towards Measuring Fairness in AI: The Casual Conversations Dataset (2021) — 123 citations
• FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture (2016) — 865 citations
• Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (2017) — 394 citations