Hamid Laga

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 1,528 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 316 citations into it; #12,930 of 21,575 by trust here, #8,831 by citations
• Artificial Intelligence — 155 citations into it
• Computational Mechanics — 65 citations into it; #6,867 of 10,001 by trust here, #6,877 by citations
• 3D Vision & Pose — 58 citations into it
• Natural Language Processing — 47 citations into it
• Aerospace Engineering — 45 citations into it
• Multimodal Learning — 43 citations into it
• Computer Graphics and Computer-Aided Design — 37 citations into it

Whom Hamid Laga trusts overall

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

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

Who trusts Hamid Laga overall

1. Laurent Valentin Jospin
2. Wray L. Buntine
3. Farid Boussaïd
4. Mohammed Bennamoun
5. Yulan Guo

Papers from Hamid Laga's perspective

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

• A Comprehensive Survey of Deep Learning for Image Captioning (2018) — 896 citations
• Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era (2019) — 417 citations
• A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation (2020) — 348 citations
• A Survey of Deep Learning Techniques for Weed Detection from Images (2021) — 409 citations
• Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users (2020) — 908 citations