Mehrdad Dianati

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 298 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it
• Automotive Engineering — 36 citations into it
• Artificial Intelligence — 32 citations into it
• Aerospace Engineering — 31 citations into it
• Environmental Engineering — 18 citations into it
• Electrical and Electronic Engineering — 15 citations into it
• 3D Vision & Pose — 11 citations into it
• Computational Mechanics — 8 citations into it

Whom Mehrdad Dianati trusts overall

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

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

Who trusts Mehrdad Dianati overall

1. Zhiguo Ding
2. Bichai Wang
3. Linglong Dai
4. Yijie Mao

Papers from Mehrdad Dianati's perspective

Ordered by how strongly the researchers Mehrdad Dianati 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. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Survey on 3D Object Detection Methods for Autonomous Driving Applications (2019) — 661 citations
• Non-Orthogonal Multiple Access (NOMA) for Future Radio Access (2017) — 335 citations
• A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications (2018) — 699 citations
• Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review (2020) — 483 citations
• Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors (2019) — 350 citations
• Non-Orthogonal Multiple Access (NOMA) for cellular future radio access (2017) — 523 citations