Hedi Ben-Younes

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 394 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #4,489 of 21,575 by trust here, #4,623 by citations
• Artificial Intelligence — 87 citations into it; #6,311 of 35,345 by trust here, #6,266 by citations
• Multimodal Learning — 47 citations into it; #228 of 1,211 by trust here, #394 by citations
• Natural Language Processing — 34 citations into it; #2,207 of 5,688 by trust here, #2,106 by citations
• Model Architectures — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Hedi Ben-Younes trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Jimmy Ba
7. Ilya Sutskever
8. Ryan Kiros
9. Ross B. Girshick
10. Yann LeCun

Who trusts Hedi Ben-Younes overall

1. Peng Gao
2. Kenneth Marino
3. Qingxing Cao
4. Chao Zhang
5. Rémi Cadène
6. Robik Shrestha
7. Duy-Kien Nguyen
8. Linjie Li
9. Preksha Nema
10. Hexiang Hu

Papers from Hedi Ben-Younes's perspective

Ordered by how strongly the researchers Hedi Ben-Younes 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• MUTAN: Multimodal Tucker Fusion for Visual Question Answering (2017) — 634 citations
• MUREL: Multimodal Relational Reasoning for Visual Question Answering (2019) — 301 citations
• RUBi: Reducing Unimodal Biases in Visual Question Answering (2019) — 442 citations