Quang-Hieu Pham

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 365 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 132 citations into it; #7,978 of 21,575 by trust here, #6,873 by citations
• Aerospace Engineering — 67 citations into it; #2,726 of 6,421 by trust here, #1,890 by citations
• Computational Mechanics — 66 citations into it; #2,614 of 10,001 by trust here, #1,122 by citations
• Geology — 56 citations into it; #565 of 687 by trust here, #267 by citations
• 3D Vision & Pose — 37 citations into it; #342 of 689 by trust here, #252 by citations
• Computer Graphics and Computer-Aided Design — 19 citations into it
• Artificial Intelligence — 18 citations into it
• Evaluation & Benchmarks — 12 citations into it; #1,732 of 3,147 by trust here, #1,489 by citations

Whom Quang-Hieu Pham trusts overall

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

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

Who trusts Quang-Hieu Pham overall

1. Lin Zhao
2. Lei Han
3. Binh-Son Hua
4. Ishan Misra
5. Rohit Girdhar
6. Zaiwei Zhang
7. Saifullahi Aminu Bello
8. Angela Dai
9. Dinh C. Nguyen
10. Yulan Guo

Papers from Quang-Hieu Pham's perspective

Ordered by how strongly the researchers Quang-Hieu Pham 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. 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
• JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields (2019) — 216 citations
• Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (2019) — 1,049 citations
• RGB-D Object-to-CAD Retrieval (2018) — 18 citations
• RGB-D to CAD Retrieval with ObjectNN Dataset (2017) — 20 citations