Omid Poursaeed

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 4 papers; trusts 572 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 193 citations into it; #14,200 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 133 citations into it; #31,091 of 35,345 by trust here, #19,439 by citations
• Generative Models — 54 citations into it; #1,775 of 1,956 by trust here, #706 by citations
• Model Architectures — 37 citations into it
• Safety, Robustness & Interpretability — 37 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Optimization & Training — 19 citations into it
• Signal Processing — 9 citations into it

Whom Omid Poursaeed trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Christian Szegedy
6. Ian J. Goodfellow
7. Yann LeCun
8. Yoshua Bengio
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Omid Poursaeed overall

1. Muzammal Naseer
2. Jamie Hayes
3. Erfan Shayegani
4. Yingwei Li
5. Liwei Song
6. Chaoning Zhang
7. George Danezis
8. Yue Dong
9. Song Bai
10. Xinwei Yu

Papers from Omid Poursaeed's perspective

Ordered by how strongly the researchers Omid Poursaeed 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Stacked Generative Adversarial Networks (2016) — 470 citations
• Generative Adversarial Perturbations (2017) — 410 citations
• Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning (2022) — 144 citations
• Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles (2023) — 437 citations