Jason Kuen

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 6 papers; trusts 1,093 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 500 citations into it; #6,409 of 21,575 by trust here, #3,558 by citations
• Artificial Intelligence — 299 citations into it
• Model Architectures — 78 citations into it
• Optimization & Training — 60 citations into it
• Representation & Self-Supervised Learning — 57 citations into it
• Natural Language Processing — 33 citations into it
• Generative Models — 22 citations into it
• Cognitive Neuroscience — 15 citations into it

Whom Jason Kuen trusts overall

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

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

Who trusts Jason Kuen overall

1. Fabien Baradel
2. Tiantian Wang
3. Junwei Han
4. Nian Liu
5. Jiuxiang Gu
6. Minki Chung
7. Christian Wolf
8. Wenguan Wang
9. Jia Li
10. Ping Hu

Papers from Jason Kuen's perspective

Ordered by how strongly the researchers Jason Kuen 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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

• Recurrent Attentional Networks for Saliency Detection (2016) — 243 citations
• Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (2018) — 29 citations
• Recent advances in convolutional neural networks (2015) — 6,013 citations
• Multimodal Contrastive Training for Visual Representation Learning (2021) — 217 citations
• Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling (2021) — 112 citations
• Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification (2018) — 486 citations