Yanming Guo

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 2,197 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 438 citations into it; #18,476 of 21,575 by trust here, #5,680 by citations
• Artificial Intelligence — 225 citations into it
• Model Architectures — 72 citations into it
• Representation & Self-Supervised Learning — 66 citations into it
• Optimization & Training — 46 citations into it
• Media Technology — 26 citations into it
• Natural Language Processing — 26 citations into it
• Generative Models — 25 citations into it

Whom Yanming Guo trusts overall

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

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

Who trusts Yanming Guo overall

1. German Ignacio Parisi
2. Aminu Da’u
3. Peter Hase
4. Stefan Wermter
5. Alexander Rakhlin
6. Yang Li
7. Anastasia Ioannidou
8. Fariborz Taherkhani
9. Yong Xue
10. Umberto Michieli

Papers from Yanming Guo's perspective

Ordered by how strongly the researchers Yanming Guo 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• CNN-RNN: a large-scale hierarchical image classification framework (2017) — 144 citations
• Adaptive Triplet Model for Fine-Grained Visual Categorization (2018) — 5 citations
• Deep learning for visual understanding (2017) — 82 citations
• A review of semantic segmentation using deep neural networks (2017) — 851 citations
• Deep learning for visual understanding: A review (2016) — 2,298 citations