Mohammad Sabokrou

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 1,014 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 283 citations into it; #7,774 of 21,575 by trust here, #9,722 by citations
• Artificial Intelligence — 191 citations into it; #11,544 of 35,345 by trust here, #10,667 by citations
• Model Architectures — 43 citations into it; #1,554 of 2,278 by trust here, #2,192 by citations
• Biomedical Engineering — 32 citations into it
• Human-Computer Interaction — 30 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Optimization & Training — 24 citations into it
• Meta & Continual Learning — 20 citations into it

Whom Mohammad Sabokrou trusts overall

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

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

Who trusts Mohammad Sabokrou overall

1. Radu Tudor Ionescu
2. Mariana-Iuliana Georgescu
3. Mohammadreza Salehi
4. Jihun Yi
5. Fahad Shahbaz Khan
6. Dilip Ravi Kiran
7. Wenpeng Hu
8. Pramuditha Perera
9. Poojan Oza
10. Stanislav Pidhorskyi

Papers from Mohammad Sabokrou's perspective

Ordered by how strongly the researchers Mohammad Sabokrou 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• STFCN: Spatio-Temporal FCN for Semantic Video Segmentation (2016) — 57 citations
• Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes (2017) — 375 citations
• Adversarially Learned One-Class Classifier for Novelty Detection (2018) — 758 citations
• Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes (2016) — 470 citations
• Looking Back on Learned Experiences For Class/task Incremental Learning (2022) — 42 citations
• Vision-based human activity recognition: a survey (2020) — 416 citations
• Driver behavior detection and classification using deep convolutional neural networks (2020) — 304 citations
• Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures (2016) — 130 citations