Oron Ashual

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 896 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 210 citations into it; #7,334 of 21,575 by trust here, #4,658 by citations
• Generative Models — 119 citations into it; #1,760 of 1,956 by trust here, #363 by citations
• Artificial Intelligence — 76 citations into it
• Multimodal Learning — 62 citations into it; #438 of 1,211 by trust here, #292 by citations
• Evaluation & Benchmarks — 30 citations into it
• Computer Graphics and Computer-Aided Design — 19 citations into it; #589 of 1,584 by trust here, #843 by citations
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Oron Ashual trusts overall

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

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

Who trusts Oron Ashual overall

1. Tianfu Wu
2. Manuel Jahn
3. Madeleine Grunde-McLaughlin
4. Stanislav Frolov
5. Zejian Li
6. Tristan Sylvain
7. Arantxa Casanova
8. Julius von Kügelgen
9. Nelson Nauata
10. Yufan Zhou

Papers from Oron Ashual's perspective

Ordered by how strongly the researchers Oron Ashual 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors (2022) — 639 citations
• KNN-Diffusion: Image Generation via Large-Scale Retrieval (2022) — 169 citations
• Specifying Object Attributes and Relations in Interactive Scene Generation (2019) — 193 citations
• Emu Edit: Precise Image Editing via Recognition and Generation Tasks (2023) — 338 citations
• Make-A-Video: Text-to-Video Generation without Text-Video Data (2022) — 2,111 citations
• Single-Shot Freestyle Dance Reenactment (2020) — 16 citations