Ferdous Sohel

Computer Vision and Pattern Recognition · 2013–2021

A researcher in the Science corpus: 16 papers; trusts 1,519 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 482 citations into it; #4,815 of 21,575 by trust here, #2,836 by citations
• Artificial Intelligence — 255 citations into it; #9,060 of 35,345 by trust here, #5,638 by citations
• Model Architectures — 77 citations into it
• Biomedical Engineering — 69 citations into it; #2,228 of 10,734 by trust here, #3,650 by citations
• Aerospace Engineering — 63 citations into it; #2,099 of 6,421 by trust here, #1,543 by citations
• Natural Language Processing — 54 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Multimodal Learning — 44 citations into it

Whom Ferdous Sohel trusts overall

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

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

Who trusts Ferdous Sohel overall

1. Jun Liu
2. Yulan Guo
3. Qiuhong Ke
4. Anastasia Ioannidou
5. Mohammed Bennamoun
6. Min Lu
7. Yifan Zhang
8. Zehua Sun
9. Aditya Mogadala
10. Amir Shahroudy

Papers from Ferdous Sohel's perspective

Ordered by how strongly the researchers Ferdous Sohel 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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

• Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data (2015) — 977 citations
• 3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014) — 606 citations
• A Comprehensive Survey of Deep Learning for Image Captioning (2018) — 896 citations
• A New Representation of Skeleton Sequences for 3D Action Recognition (2017) — 866 citations
• A Comprehensive Performance Evaluation of 3D Local Feature Descriptors (2015) — 578 citations
• Rotational Projection Statistics for 3D Local Surface Description and Object Recognition (2013) — 668 citations
• Learning Clip Representations for Skeleton-Based 3D Action Recognition (2018) — 258 citations
• Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition (2018) — 14 citations
• Computer Vision for Human-Machine Interaction (2018) — 79 citations
• Contractive Rectifier Networks for Nonlinear Maximum Margin Classification (2015) — 12 citations
• A Survey of Deep Learning Techniques for Weed Detection from Images (2021) — 409 citations
• SkeletonNet: Mining Deep Part Features for 3-D Action Recognition (2017) — 158 citations
• Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction (2016) — 40 citations
• Spatial, Structural and Temporal Feature Learning for Human Interaction Prediction (2016) — 11 citations
• Human Interaction Prediction Using Deep Temporal Features (2016) — 44 citations
• Automatic Feature Learning for Robust Shadow Detection (2014) — 153 citations