Tuan-Hung Vu

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 5 papers; trusts 941 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 138 citations into it; #5,479 of 21,575 by trust here, #5,287 by citations
• Artificial Intelligence — 92 citations into it; #9,451 of 35,345 by trust here, #8,077 by citations
• Representation & Self-Supervised Learning — 31 citations into it; #1,720 of 3,145 by trust here, #1,460 by citations
• Evaluation & Benchmarks — 20 citations into it
• Radiology, Nuclear Medicine and Imaging — 20 citations into it; #932 of 3,265 by trust here, #934 by citations
• Optimization & Training — 16 citations into it
• Generative Models — 11 citations into it
• Model Architectures — 11 citations into it

Whom Tuan-Hung Vu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Andrew Zisserman
7. Ross B. Girshick
8. L. Eon Bottou
9. Yoshua Bengio
10. Yann LeCun

Who trusts Tuan-Hung Vu overall

1. Lukas Hoyer
2. Jian Liang
3. Siddhartha Chandra
4. Arthur Douillard
5. Dengxin Dai
6. Dapeng Hu
7. Xiangde Luo
8. Umberto Michieli
9. Garrett Wilson
10. Luke Melas-Kyriazi

Papers from Tuan-Hung Vu's perspective

Ordered by how strongly the researchers Tuan-Hung Vu 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. 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

• Zero-Shot Semantic Segmentation (2019) — 406 citations
• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation (2018) — 1,529 citations
• Context-Aware CNNs for Person Head Detection (2015) — 146 citations
• Predicting Actions from Static Scenes (2014) — 66 citations
• ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation (2020) — 50 citations