Dinh C. Nguyen
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 18 papers; trusts 1,242 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 428 citations into it; #6,447 of 21,575 by trust here, #5,236 by citations
- Artificial Intelligence — 305 citations into it
- Computer Networks and Communications — 142 citations into it; #6,241 of 9,886 by trust here, #3,074 by citations
- Information Systems — 126 citations into it; #5,314 of 8,771 by trust here, #2,912 by citations
- Electrical and Electronic Engineering — 110 citations into it
- Aerospace Engineering — 107 citations into it; #1,716 of 6,421 by trust here, #1,573 by citations
- Computational Mechanics — 69 citations into it; #2,657 of 10,001 by trust here, #1,122 by citations
- Geology — 66 citations into it; #564 of 687 by trust here, #267 by citations
Whom Dinh C. Nguyen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Navneet Dalal
- Pedro F. Felzenszwalb
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- David G. Lowe
- Ross B. Girshick
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
Who trusts Dinh C. Nguyen overall
- Binh-Son Hua
- Angela Dai
- Ming Ding
- Quang-Hieu Pham
- Mike Roberts
- Pubudu N. Pathirana
- Wengling Chen
- Quoc-Viet Pham
- Minh-Khoi Tran
- Ligang Zhang
Papers from Dinh C. Nguyen's perspective
Ordered by how strongly the researchers Dinh C. Nguyen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- 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
- Deep learning for deepfakes creation and detection: A survey (2019) — 629 citations
- Blockchain for 5G and Beyond Networks: A State of the Art Survey (2019) — 342 citations
- Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021) — 595 citations
- Human detection from images and videos: A survey (2016) — 267 citations
- 6G Internet of Things: A Comprehensive Survey (2021) — 1,268 citations
- Federated Learning for Smart Healthcare: A Survey (2021) — 872 citations
- Federated Learning for Internet of Things: A Comprehensive Survey (2021) — 1,406 citations
- Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020) — 325 citations
- A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020) — 484 citations
- Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) — 398 citations
- Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (2019) — 1,049 citations
- Deep Learning for Deepfakes Creation and Detection (2019) — 150 citations
- RGB-D Object-to-CAD Retrieval (2018) — 18 citations
- RGB-D to CAD Retrieval with ObjectNN Dataset (2017) — 20 citations