Nassim Ammour

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 261 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 55 citations into it; #15,977 of 21,575 by trust here, #21,103 by citations
• Media Technology — 35 citations into it; #2,353 of 3,621 by trust here, #3,192 by citations
• Artificial Intelligence — 33 citations into it; #24,614 of 35,345 by trust here, #31,361 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Ecology — 8 citations into it
• Meta & Continual Learning — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Nassim Ammour trusts overall

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

1. Zhizhong Li
2. Qi Wang
3. Kaiming He
4. Lijun Zhao
5. Gui-Song Xia
6. Derek Hoiem
7. Geoffrey E. Hinton
8. Alex Krizhevsky
9. Gong Cheng
10. Ross B. Girshick

Who trusts Nassim Ammour overall

1. Valerio Marsocci
2. S. Divakar Bhat
3. Simone Scardapane
4. Biplab Banerjee

Papers from Nassim Ammour's perspective

Ordered by how strongly the researchers Nassim Ammour 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. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Long Short-Term Memory

Their papers

• Continual Learning Using Data Regeneration for Remote Sensing Scene Classification (2022) — 17 citations
• A Contrastive Continual Learning for the Classification of Remote Sensing Imagery (2022) — 5 citations
• Continual learning using EfficientNet and data generation for remote sensing image classification (2022)
• Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021) — 298 citations
• Continual Learning Approach for Remote Sensing Scene Classification (2020) — 20 citations