Feras Dayoub

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 921 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #6,841 of 21,575 by trust here, #4,750 by citations
• Artificial Intelligence — 115 citations into it; #8,733 of 35,345 by trust here, #12,244 by citations
• Aerospace Engineering — 29 citations into it; #3,322 of 6,421 by trust here, #1,815 by citations
• Evaluation & Benchmarks — 29 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Safety, Robustness & Interpretability — 19 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 12 citations into it

Whom Feras Dayoub trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Radford M. Neal
7. Ilya Sutskever
8. Yoshua Bengio
9. Shaoqing Ren
10. Diederik P. Kingma

Who trusts Feras Dayoub overall

1. Niko Sünderhauf
2. Di Feng
3. Xuefeng Du
4. Juan R. Terven
5. Dimity Miller
6. Roberto Arroyo
7. K. J. Joseph
8. Fatih Cagatay Akyon
9. Michael Milford
10. David Hall

Papers from Feras Dayoub's perspective

Ordered by how strongly the researchers Feras Dayoub 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. Long Short-Term Memory
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

• On the performance of ConvNet features for place recognition (2015) — 555 citations
• Dropout Sampling for Robust Object Detection in Open-Set Conditions (2017) — 272 citations
• Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free (2015) — 409 citations
• Place categorization and semantic mapping on a mobile robot (2015) — 144 citations
• DeepFruits: A Fruit Detection System Using Deep Neural Networks (2016) — 1,000 citations
• VarifocalNet: An IoU-aware Dense Object Detector (2020) — 1,032 citations
• Probabilistic Object Detection: Definition and Evaluation (2018) — 128 citations
• Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection (2018) — 119 citations
• Episode-Based Active Learning with Bayesian Neural Networks (2017) — 10 citations