Chengkai Zhang

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 6 papers; trusts 731 researchers and is trusted by 623 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 258 citations into it; #1,733 of 21,575 by trust here, #2,130 by citations
• Computational Mechanics — 102 citations into it; #812 of 10,001 by trust here, #228 by citations
• 3D Vision & Pose — 89 citations into it; #361 of 689 by trust here, #287 by citations
• Aerospace Engineering — 62 citations into it
• Computer Graphics and Computer-Aided Design — 53 citations into it
• Generative Models — 52 citations into it; #469 of 1,956 by trust here, #325 by citations
• Artificial Intelligence — 42 citations into it
• Geology — 30 citations into it; #464 of 687 by trust here, #217 by citations

Whom Chengkai Zhang trusts overall

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

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

Who trusts Chengkai Zhang overall

1. Jiajun Wu
2. Zhiqin Chen
3. Xian-Feng Han
4. Bram Wallace
5. Jun-Yan Zhu
6. Georgia Gkioxari
7. Julian Chibane
8. Edward James Smith
9. Jitendra Malik
10. Maxim Tatarchenko

Papers from Chengkai Zhang's perspective

Ordered by how strongly the researchers Chengkai 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

• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016) — 2,154 citations
• 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
• A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding (2016) — 53 citations
• Learning Shape Priors for Single-View 3D Completion and Reconstruction (2018) — 201 citations