Huikai Wu

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 646 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #13,099 of 21,575 by trust here, #9,722 by citations
• Artificial Intelligence — 27 citations into it
• Generative Models — 22 citations into it; #1,436 of 1,956 by trust here, #1,435 by citations
• Model Architectures — 10 citations into it
• Computer Graphics and Computer-Aided Design — 9 citations into it
• Optimization & Training — 6 citations into it

Whom Huikai Wu trusts overall

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

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

Who trusts Huikai Wu overall

1. Yongjun Hong
2. Ruoteng Li
3. Yuval Nirkin
4. Abdul Jabbar
5. Shunyi Zheng
6. Uiwon Hwang
7. J. Liew
8. Marco Forte
9. Jie Gui
10. Elizaveta Logacheva

Papers from Huikai Wu's perspective

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

Their papers

• Fast End-to-End Trainable Guided Filter (2018) — 266 citations
• GP-GAN: Towards Realistic High-Resolution Image Blending (2017) — 282 citations
• FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation (2019) — 340 citations