Yan Yan

Computer Vision and Pattern Recognition · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 10 papers; trusts 986 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 214 citations into it; #6,902 of 21,575 by trust here, #4,623 by citations
• Artificial Intelligence — 107 citations into it; #20,393 of 35,345 by trust here, #11,327 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Model Architectures — 20 citations into it
• Biomedical Engineering — 18 citations into it
• Signal Processing — 18 citations into it; #4,994 of 7,572 by trust here, #4,373 by citations
• Optimization & Training — 17 citations into it
• Aerospace Engineering — 16 citations into it

Whom Yan Yan trusts overall

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

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

Who trusts Yan Yan overall

1. Xuanyi Dong
2. Radu Tudor Ionescu
3. Yanli Liu
4. Fangyu Zou
5. Yutian Lin
6. Xinmeng Huang
7. Dan Xu
8. Rong Jin
9. Hao Yu
10. Yi Xu

Papers from Yan Yan's perspective

Ordered by how strongly the researchers Yan Yan 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. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Style Aggregated Network for Facial Landmark Detection (2018) — 324 citations
• Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015) — 541 citations
• Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation (2019) — 322 citations
• Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning (2018) — 377 citations
• A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification (2019) — 583 citations
• Detecting anomalous events in videos by learning deep representations of appearance and motion (2017) — 464 citations
• A Unified Analysis of Stochastic Momentum Methods for Deep Learning (2018) — 140 citations
• Where am I in the dark: Exploring active transfer learning on the use of indoor localization based on thermal imaging (2016) — 81 citations
• Hybrid Heterogeneous Transfer Learning through Deep Learning (2014) — 181 citations
• No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion (2013) — 120 citations