Stéphane Herbin

Computer Vision and Pattern Recognition · 2012–2021

A researcher in the Science corpus: 5 papers; trusts 882 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #13,407 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 100 citations into it; #9,640 of 35,345 by trust here, #12,244 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Multimodal Learning — 17 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Radiology, Nuclear Medicine and Imaging — 15 citations into it; #1,087 of 3,265 by trust here, #1,783 by citations
• Meta & Continual Learning — 13 citations into it

Whom Stéphane Herbin trusts overall

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

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

Who trusts Stéphane Herbin overall

1. Max Bucher
2. Vinay Kumar Verma
3. Yongqin Xian
4. Mert Bulent Sariyildiz
5. Li Zhang
6. Piyush Rai
7. Shafin Rahman
8. Ali Cheraghian
9. Wei Wang
10. Rafael Felix

Papers from Stéphane Herbin's perspective

Ordered by how strongly the researchers Stéphane Herbin 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
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

• Generating Visual Representations for Zero-Shot Classification (2017) — 158 citations
• Semantic hierarchies for image annotation: A survey (2012) — 173 citations
• Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classiffication (2016) — 220 citations
• Semantic bottleneck for computer vision tasks (2018) — 18 citations
• Semi-Supervised Class Incremental Learning (2021) — 15 citations