Farid Boussaïd

Computer Vision and Pattern Recognition · 2013–2022

A researcher in the Science corpus: 14 papers; trusts 1,319 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 386 citations into it; #5,748 of 21,575 by trust here, #5,620 by citations
• Artificial Intelligence — 238 citations into it; #9,902 of 35,345 by trust here, #9,870 by citations
• Model Architectures — 76 citations into it
• Biomedical Engineering — 67 citations into it; #2,229 of 10,734 by trust here, #3,650 by citations
• Representation & Self-Supervised Learning — 53 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it
• 3D Vision & Pose — 37 citations into it; #658 of 689 by trust here, #485 by citations
• Human-Computer Interaction — 31 citations into it

Whom Farid Boussaïd trusts overall

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

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

Who trusts Farid Boussaïd overall

1. Jun Liu
2. Quynh N. Nguyen
3. Qiuhong Ke
4. Matthias Hein
5. Yifan Zhang
6. Mohammed Bennamoun
7. Zehua Sun
8. Jeremiah Zhe Liu

Papers from Farid Boussaïd's perspective

Ordered by how strongly the researchers Farid Boussaïd trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A New Representation of Skeleton Sequences for 3D Action Recognition (2017) — 866 citations
• A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation (2020) — 348 citations
• Multi-class Token Transformer for Weakly Supervised Semantic Segmentation (2022) — 302 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
• Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users (2020) — 908 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
• How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (2015) — 28 citations
• 3D-Div: A novel local surface descriptor for feature matching and pairwise range image registration (2013) — 22 citations