Mihai Datcu

Computer Vision and Pattern Recognition · 2000–2021

A researcher in the Science corpus: 10 papers; trusts 795 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it; #4,541 of 21,575 by trust here, #2,739 by citations
• Artificial Intelligence — 54 citations into it; #19,449 of 35,345 by trust here, #8,365 by citations
• Media Technology — 46 citations into it; #452 of 3,621 by trust here, #211 by citations
• Evaluation & Benchmarks — 23 citations into it; #2,347 of 3,147 by trust here, #1,262 by citations
• Aerospace Engineering — 16 citations into it
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #2,531 of 3,145 by trust here, #2,910 by citations
• Ocean Engineering — 11 citations into it

Whom Mihai Datcu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Andrew Zisserman
4. David G. Lowe
5. David M. Blei
6. Kaiming He
7. Yann LeCun
8. Yoshua Bengio
9. Ross B. Girshick
10. Diederik P. Kingma

Who trusts Mihai Datcu overall

1. Liangpei Zhang
2. Yanfei Zhong
3. Gong Cheng
4. Chaopeng Shen
5. Bei Zhao
6. Jamie Sherrah
7. Gui-Song Xia
8. Junwei Han
9. Jian Kang
10. Ava Vali

Papers from Mihai Datcu's perspective

Ordered by how strongly the researchers Mihai Datcu 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. 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• DOTA: A Large-Scale Dataset for Object Detection in Aerial Images (2017) — 2,943 citations
• Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection (2016) — 665 citations
• SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS (2016) — 334 citations
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges (2021) — 655 citations
• Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks (2016) — 581 citations
• Application and Evaluation of a Hierarchical Patch Clustering Method for Remote Sensing Images (2016) — 12 citations
• Remote Sensing Image Classification: No Features, No Clustering (2015) — 36 citations
• A Comparative Study of Bag-of-Words and Bag-of-Topics Models of EO Image Patches (2015) — 58 citations
• Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation (2010) — 277 citations
• Interactive learning and probabilistic retrieval in remote sensing image archives (2000) — 153 citations