Hassan Foroosh

Computer Vision and Pattern Recognition · 2002–2020

A researcher in the Science corpus: 9 papers; trusts 773 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it; #4,938 of 21,575 by trust here, #3,848 by citations
• Artificial Intelligence — 64 citations into it
• Model Architectures — 34 citations into it; #730 of 2,278 by trust here, #600 by citations
• Efficiency & Compression — 26 citations into it; #356 of 1,264 by trust here, #355 by citations
• Computational Mechanics — 18 citations into it; #2,682 of 10,001 by trust here, #1,031 by citations
• Optimization & Training — 15 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Hassan Foroosh trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Yoshua Bengio
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. X. Zhang

Who trusts Hassan Foroosh overall

1. Jiayi Ma
2. Xingyu Jiang
3. Dongming Feng
4. Swarun Kumar
5. Wujie Wen
6. Rahaf Aljundi
7. José Manuel Álvarez
8. Manoj Ramanathan
9. Daniel Weinland
10. Haichuan Yang

Papers from Hassan Foroosh's perspective

Ordered by how strongly the researchers Hassan Foroosh 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. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Sparse Convolutional Neural Networks (2015) — 924 citations
• Factorized Convolutional Neural Networks (2016) — 188 citations
• View-invariant action recognition using fundamental ratios (2008) — 85 citations
• Slim-CNN: A Light-Weight CNN for Face Attribute Prediction (2019) — 52 citations
• ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples (2018) — 331 citations
• View-Invariant Action Recognition from Point Triplets (2009) — 70 citations
• PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (2020) — 610 citations
• Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure (2016) — 53 citations
• Extension of phase correlation to subpixel registration (2002) — 1,081 citations