Atousa Torabi

Computer Vision and Pattern Recognition · 2012–2016

A researcher in the Science corpus: 6 papers; trusts 570 researchers and is trusted by 892 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 151 citations into it; #1,438 of 21,575 by trust here, #2,091 by citations
• Artificial Intelligence — 82 citations into it
• Natural Language Processing — 56 citations into it; #2,476 of 5,688 by trust here, #3,826 by citations
• Multimodal Learning — 53 citations into it; #110 of 1,211 by trust here, #222 by citations
• Evaluation & Benchmarks — 31 citations into it
• Model Architectures — 22 citations into it
• Optimization & Training — 8 citations into it
• Large Language Models — 6 citations into it

Whom Atousa Torabi trusts overall

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

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

Who trusts Atousa Torabi overall

1. Youngjae Yu
2. Anna Rohrbach
3. Antoine Miech
4. Kyunghyun Cho
5. Rowan Zellers
6. Subhashini Venugopalan
7. Lisa Anne Hendricks
8. Marcus Rohrbach
9. Yunchen Pu
10. Yoshua Bengio

Papers from Atousa Torabi's perspective

Ordered by how strongly the researchers Atousa Torabi 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. 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

• Describing Videos by Exploiting Temporal Structure (2015) — 1,108 citations
• Movie Description (2016) — 423 citations
• Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research (2015) — 216 citations
• Learning Language-Visual Embedding for Movie Understanding with Natural-Language (2016) — 110 citations
• Combining modality specific deep neural networks for emotion recognition in video (2013) — 397 citations
• An iterative integrated framework for thermal-visible image registration, sensor fusion, and people tracking for video surveillance applications (2012) — 165 citations