Meina Kan

Computer Vision and Pattern Recognition · 2012–2020

A researcher in the Science corpus: 6 papers; trusts 732 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 164 citations into it; #8,950 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 82 citations into it; #31,277 of 35,345 by trust here, #20,377 by citations
• Generative Models — 35 citations into it
• Efficiency & Compression — 22 citations into it
• Model Architectures — 22 citations into it
• Signal Processing — 17 citations into it
• Optimization & Training — 13 citations into it
• Media Technology — 7 citations into it

Whom Meina Kan trusts overall

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

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

Who trusts Meina Kan overall

1. Cuicui Kang
2. Changxing Ding
3. Peng Hu
4. Ran He
5. Jing Zhao
6. Dacheng Tao
7. Ruizhe Zhao
8. Shengcai Liao
9. Liangli Zhen
10. Yu Zheng

Papers from Meina Kan's perspective

Ordered by how strongly the researchers Meina Kan 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment (2014) — 545 citations
• Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation (2020) — 776 citations
• AttGAN: Facial Attribute Editing by Only Changing What You Want (2017) — 828 citations
• Arbitrary Facial Attribute Editing: Only Change What You Want (2017) — 57 citations
• Fully Learnable Group Convolution for Acceleration of Deep Neural Networks (2019) — 81 citations
• Multi-View Discriminant Analysis (2012) — 702 citations