Binh-Son Hua

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 11 papers; trusts 1,017 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 289 citations into it; #6,743 of 21,575 by trust here, #6,031 by citations
• Computational Mechanics — 139 citations into it; #1,441 of 10,001 by trust here, #665 by citations
• Geology — 116 citations into it; #247 of 687 by trust here, #129 by citations
• Aerospace Engineering — 115 citations into it; #1,388 of 6,421 by trust here, #978 by citations
• 3D Vision & Pose — 67 citations into it; #374 of 689 by trust here, #252 by citations
• Artificial Intelligence — 49 citations into it
• Environmental Engineering — 41 citations into it; #2,589 of 5,479 by trust here, #1,430 by citations
• Computer Graphics and Computer-Aided Design — 38 citations into it

Whom Binh-Son Hua trusts overall

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

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

Who trusts Binh-Son Hua overall

1. Quang-Hieu Pham
2. Dinh C. Nguyen
3. Saifullahi Aminu Bello
4. Angela Dai
5. Zaiwei Zhang
6. Sai-Kit Yeung
7. Jens Behley
8. Jaesik Park
9. Qian-Yi Zhou
10. Thang Vu

Papers from Binh-Son Hua's perspective

Ordered by how strongly the researchers Binh-Son Hua 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. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• SceneNN: A Scene Meshes Dataset with aNNotations (2016) — 406 citations
• A Field Model for Repairing 3D Shapes (2016) — 122 citations
• A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation (2016) — 46 citations
• JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields (2019) — 216 citations
• HyperInverter: Improving StyleGAN Inversion via Hypernetwork (2021) — 129 citations
• Rotation Invariant Convolutions for 3D Point Clouds Deep Learning (2019) — 199 citations
• Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (2019) — 1,049 citations
• ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics (2019) — 407 citations
• RGB-D Object-to-CAD Retrieval (2018) — 18 citations
• Pointwise Convolutional Neural Networks (2017) — 587 citations
• RGB-D to CAD Retrieval with ObjectNN Dataset (2017) — 20 citations