Amirreza Shaban

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 461 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #11,767 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 55 citations into it; #13,489 of 35,345 by trust here, #10,667 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Optimization & Training — 15 citations into it
• Signal Processing — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Model Architectures — 8 citations into it
• Computational Mechanics — 7 citations into it; #6,794 of 10,001 by trust here, #6,041 by citations

Whom Amirreza Shaban trusts overall

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

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

Who trusts Amirreza Shaban overall

1. Chi Zhang
2. Mennatullah Siam
3. Xuanyi Dong
4. Chelsea Finn
5. Guangchen Shi
6. Aravind Rajeswaran
7. Timothy M. Hospedales
8. Guosheng Lin
9. Timo Lüddecke
10. Khoi Duc Minh Nguyen

Papers from Amirreza Shaban's perspective

Ordered by how strongly the researchers Amirreza Shaban 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• One-Shot Learning for Semantic Segmentation (2017) — 897 citations
• Truncated Back-propagation for Bilevel Optimization (2018) — 334 citations
• MMTM: Multimodal Transfer Module for CNN Fusion (2019) — 399 citations