Yong-Liang Yang

Computer Vision and Pattern Recognition · 2006–2020

A researcher in the Science corpus: 4 papers; trusts 499 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 131 citations into it; #6,525 of 21,575 by trust here, #6,426 by citations
• Generative Models — 46 citations into it
• Computational Mechanics — 33 citations into it; #5,475 of 10,001 by trust here, #1,985 by citations
• Computer Graphics and Computer-Aided Design — 32 citations into it; #441 of 1,584 by trust here, #329 by citations
• Artificial Intelligence — 31 citations into it
• Aerospace Engineering — 17 citations into it
• Optimization & Training — 7 citations into it

Whom Yong-Liang Yang trusts overall

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

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

Who trusts Yong-Liang Yang overall

1. Michael Niemeyer
2. Eric Chan
3. Peng Wang

Papers from Yong-Liang Yang's perspective

Ordered by how strongly the researchers Yong-Liang Yang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. ImageNet Large Scale Visual Recognition Challenge
8. Long Short-Term Memory
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• HoloGAN: Unsupervised Learning of 3D Representations From Natural Images (2019) — 554 citations
• BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images (2020) — 253 citations
• Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation (2020) — 164 citations
• Geometric Modeling with Conical Meshes and Developable Surfaces (2006) — 476 citations