Zhoutong Zhang

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 807 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 291 citations into it; #8,028 of 21,575 by trust here, #5,620 by citations
• Computational Mechanics — 109 citations into it; #3,716 of 10,001 by trust here, #1,160 by citations
• 3D Vision & Pose — 94 citations into it; #362 of 689 by trust here, #287 by citations
• Computer Graphics and Computer-Aided Design — 74 citations into it
• Artificial Intelligence — 62 citations into it
• Generative Models — 58 citations into it
• Aerospace Engineering — 54 citations into it
• Evaluation & Benchmarks — 28 citations into it

Whom Zhoutong Zhang 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. David G. Lowe

Who trusts Zhoutong Zhang overall

1. Xian-Feng Han
2. Georgia Gkioxari
3. Michael Oechsle
4. Julian Chibane
5. Jiajun Wu
6. Lars M. Mescheder
7. Philipp Henzler
8. Hamid Laga
9. Linqi Zhou
10. Jitendra Malik

Papers from Zhoutong Zhang's perspective

Ordered by how strongly the researchers Zhoutong Zhang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018) — 524 citations
• Visual Object Networks: Image Generation with Disentangled 3D Representations (2018) — 273 citations
• Learning to Reconstruct Shapes from Unseen Classes (2018) — 164 citations
• Unsupervised Semantic Segmentation by Distilling Feature Correspondences (2022) — 334 citations
• Learning Shape Priors for Single-View 3D Completion and Reconstruction (2018) — 201 citations
• Editing Conditional Radiance Fields (2021) — 300 citations