Kuiyuan Yang

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 8 papers; trusts 1,036 researchers and is trusted by 70 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 226 citations into it; #3,688 of 21,575 by trust here, #2,401 by citations
• Artificial Intelligence — 106 citations into it; #11,155 of 35,345 by trust here, #5,153 by citations
• Evaluation & Benchmarks — 33 citations into it
• Model Architectures — 33 citations into it; #1,269 of 2,278 by trust here, #794 by citations
• Representation & Self-Supervised Learning — 22 citations into it; #1,144 of 3,145 by trust here, #2,355 by citations
• Media Technology — 16 citations into it
• Optimization & Training — 12 citations into it
• Natural Language Processing — 9 citations into it

Whom Kuiyuan Yang trusts overall

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

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

Who trusts Kuiyuan Yang overall

1. Umberto Michieli
2. Andrew Zhai
3. Xiangteng He
4. Kai Han
5. Kevin Musgrave
6. Yuxin Peng
7. Heliang Zheng
8. Weifeng Ge
9. Yanming Guo
10. Simone Scardapane

Papers from Kuiyuan Yang's perspective

Ordered by how strongly the researchers Kuiyuan Yang 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The application of two-level attention models in deep convolutional neural network for fine-grained image classification (2014) — 824 citations
• DenseASPP for Semantic Segmentation in Street Scenes (2018) — 1,473 citations
• Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification (2014) — 256 citations
• Hard-Aware Deeply Cascaded Embedding (2016) — 314 citations
• GFF: Gated Fully Fusion for Semantic Segmentation (2019) — 238 citations
• Semantic Flow for Fast and Accurate Scene Parsing (2020) — 448 citations
• Visualizing and Comparing Convolutional Neural Networks (2014) — 54 citations
• Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network (2015) — 32 citations