Zhuwen Li

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 660 researchers and is trusted by 217 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #2,605 of 21,575 by trust here, #3,251 by citations
• Computational Mechanics — 77 citations into it; #2,841 of 10,001 by trust here, #684 by citations
• 3D Vision & Pose — 66 citations into it; #355 of 689 by trust here, #167 by citations
• Computer Graphics and Computer-Aided Design — 61 citations into it; #695 of 1,584 by trust here, #222 by citations
• Aerospace Engineering — 37 citations into it
• Artificial Intelligence — 25 citations into it; #18,675 of 35,345 by trust here, #19,439 by citations
• Generative Models — 20 citations into it; #959 of 1,956 by trust here, #573 by citations
• Geology — 20 citations into it

Whom Zhuwen Li trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Vladlen Koltun
4. Alex Krizhevsky
5. Kaiming He
6. Yann LeCun
7. Yoshua Bengio
8. Philipp Krahenbuhl
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Zhuwen Li overall

1. Konstantin Sofiiuk
2. Andreas Loukas
3. J. Liew
4. Zheng Lin
5. Scott D. Cohen
6. Soumajit Majumder
7. Zhiqing Sun
8. Won-Dong Jang
9. Guillem Brasó
10. Brian L. Price

Papers from Zhuwen Li's perspective

Ordered by how strongly the researchers Zhuwen Li 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images (2018) — 1,502 citations
• What Do Single-View 3D Reconstruction Networks Learn? (2019) — 502 citations
• Interactive Image Segmentation with Latent Diversity (2018) — 221 citations
• Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation (2019) — 285 citations
• Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search (2018) — 561 citations
• Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness (2019) — 378 citations