Zeng Huang

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 319 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 120 citations into it; #6,953 of 21,575 by trust here, #4,926 by citations
• Computational Mechanics — 68 citations into it; #2,460 of 10,001 by trust here, #826 by citations
• Computer Graphics and Computer-Aided Design — 49 citations into it
• 3D Vision & Pose — 38 citations into it; #328 of 689 by trust here, #200 by citations
• Generative Models — 10 citations into it

Whom Zeng Huang trusts overall

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

1. Kaiming He
2. Michael J. Black
3. Geoffrey E. Hinton
4. Jitendra Malik
5. Alex Krizhevsky
6. Karen Simonyan
7. Diederik P. Kingma
8. Ross B. Girshick
9. Yasutaka Furukawa
10. Yann LeCun

Who trusts Zeng Huang overall

1. Zerong Zheng
2. Michael Niemeyer
3. Yuliang Xiu
4. Philipp Henzler
5. Sida Peng
6. Yebin Liu
7. Lars M. Mescheder
8. Michael Oechsle
9. Shunsuke Saito
10. Jinlong Yang

Papers from Zeng Huang's perspective

Ordered by how strongly the researchers Zeng Huang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization (2019) — 1,437 citations
• Deep Volumetric Video From Very Sparse Multi-view Performance Capture (2018) — 128 citations
• ARCH: Animatable Reconstruction of Clothed Humans (2020) — 384 citations