Jun Wan

Computer Vision and Pattern Recognition · with Li · 2016–2019

A researcher in the Science corpus: 8 papers; trusts 1,068 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 308 citations into it; #5,977 of 21,575 by trust here, #7,027 by citations
• Artificial Intelligence — 133 citations into it
• Human-Computer Interaction — 82 citations into it; #274 of 2,253 by trust here, #428 by citations
• Biomedical Engineering — 75 citations into it; #2,694 of 10,734 by trust here, #6,455 by citations
• Model Architectures — 36 citations into it
• Signal Processing — 33 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Jun Wan trusts overall

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

1. David G. Lowe
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Kaiming He
5. Alex Krizhevsky
6. Ivan Laptev
7. Ross B. Girshick
8. Andrew Zisserman
9. Yoshua Bengio
10. Yann LeCun

Who trusts Jun Wan overall

1. Pichao Wang
2. Jiali Duan
3. Guangming Zhu
4. Wanqing Li
5. Yifan Zhang
6. Shuai Zhou
7. Sergio Escalera
8. S. Li
9. Zhimin Gao
10. Xiujuan Chai

Papers from Jun Wan's perspective

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

Their papers

• Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016) — 102 citations
• Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition (2016) — 39 citations
• CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019) — 203 citations
• A Unified Framework for Multi-Modal Isolated Gesture Recognition (2018) — 79 citations
• Learning Approximate Stochastic Transition Models (2017) — 25 citations
• RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017) — 384 citations
• ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview (2016) — 89 citations
• ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition (2016) — 247 citations