Hui Huang

Computer Vision and Pattern Recognition · ACM Transactions on Graphics · 2009–2022

A researcher in the Science corpus: 11 papers; trusts 474 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 159 citations into it; #13,556 of 21,575 by trust here, #11,083 by citations
• Computational Mechanics — 110 citations into it; #2,214 of 10,001 by trust here, #1,900 by citations
• Computer Graphics and Computer-Aided Design — 77 citations into it; #349 of 1,584 by trust here, #435 by citations
• Artificial Intelligence — 39 citations into it; #29,170 of 35,345 by trust here, #20,836 by citations
• 3D Vision & Pose — 37 citations into it; #593 of 689 by trust here, #639 by citations
• Aerospace Engineering — 35 citations into it
• Analytical Chemistry — 35 citations into it
• Model Architectures — 18 citations into it

Whom Hui Huang trusts overall

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

1. M. Kazhdan
2. Hugues Hoppe
3. Aoife A. Gowen
4. Daniel Cohen-Or
5. Kaiming He
6. Nina Amenta
7. Markus H. Gross
8. Alex Krizhevsky
9. Geoffrey E. Hinton
10. Jianwei Qin

Who trusts Hui Huang overall

1. Shihao Wu
2. Yifan Wang
3. Lequan Yu
4. Daniel Cohen-Or
5. Kang Yin
6. Chi-Wing Fu
7. Ruihui Li
8. Simon Fraser
9. P. Heng
10. Alexandre Boulch

Papers from Hui Huang's perspective

Ordered by how strongly the researchers Hui Huang 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Multi-scale Context Intertwining for Semantic Segmentation (2018) — 171 citations
• Patch-Based Progressive 3D Point Set Upsampling (2018) — 321 citations
• Data-driven contextual modeling for 3D scene understanding (2016) — 58 citations
• UrbanScene3D: A Large Scale Urban Scene Dataset and Simulator (2021) — 30 citations
• Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014) — 350 citations
• ShapeFormer: Transformer-based Shape Completion via Sparse Representation (2022) — 167 citations
• Assessment of intramuscular fat content of pork using NIR hyperspectral images of rib end (2017) — 48 citations
• Deep points consolidation (2015) — 138 citations
• Edge-aware point set resampling (2013) — 478 citations
• L1-medial skeleton of point cloud (2013) — 361 citations
• Consolidation of unorganized point clouds for surface reconstruction (2009) — 522 citations