Zhixin Shu

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 334 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 142 citations into it; #21,390 of 21,575 by trust here, #6,873 by citations
• Computer Graphics and Computer-Aided Design — 43 citations into it
• 3D Vision & Pose — 31 citations into it
• Generative Models — 24 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Artificial Intelligence — 9 citations into it

Whom Zhixin Shu trusts overall

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

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

Who trusts Zhixin Shu overall

1. Mihir Sahasrabudhe
2. Xiang Wang
3. Zhengqi Li
4. Soumyadip Sengupta
5. Yuheng Li
6. Zhengqin Li
7. Jie Gui
8. Shangzhe Wu
9. Hao Zhou
10. Edo Collins

Papers from Zhixin Shu's perspective

Ordered by how strongly the researchers Zhixin Shu 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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

• Neural Face Editing with Intrinsic Image Disentangling (2017) — 295 citations
• Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance (2018) — 211 citations
• Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model Using Deep Non-Rigid Structure From Motion (2019) — 43 citations
• Point-NeRF: Point-based Neural Radiance Fields (2022) — 755 citations