Mojtaba Seyedhosseini

Computer Vision and Pattern Recognition · 2013–2023

A researcher in the Science corpus: 6 papers; trusts 891 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 273 citations into it; #11,121 of 21,575 by trust here, #3,393 by citations
• Artificial Intelligence — 204 citations into it; #23,809 of 35,345 by trust here, #4,512 by citations
• Multimodal Learning — 83 citations into it
• Evaluation & Benchmarks — 47 citations into it
• Natural Language Processing — 40 citations into it
• Model Architectures — 28 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Optimization & Training — 14 citations into it

Whom Mojtaba Seyedhosseini trusts overall

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

1. Piotr Doll'ar
2. Geoffrey E. Hinton
3. Pablo Arbeláez
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Jitendra Malik
8. Antonio Torralba
9. Ross B. Girshick
10. Yoshua Bengio

Who trusts Mojtaba Seyedhosseini overall

1. Amos Sironi
2. Hao Zhu
3. Tolga Tasdizen
4. Vincent Lepetit
5. Ka-Veng Yuen
6. Pascal Fua
7. Lyudmila S. Mihaylova

Papers from Mojtaba Seyedhosseini's perspective

Ordered by how strongly the researchers Mojtaba Seyedhosseini 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• CoCa: Contrastive Captioners are Image-Text Foundation Models (2022) — 1,785 citations
• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks (2013) — 92 citations
• Crowdsourcing the creation of image segmentation algorithms for connectomics (2015) — 371 citations
• Semantic Image Segmentation with Contextual Hierarchical Models (2015) — 56 citations