David S. Doermann

Computer Vision and Pattern Recognition · 2000–2025

A researcher in the Science corpus: 10 papers; trusts 694 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 242 citations into it; #4,372 of 21,575 by trust here, #4,048 by citations
• Artificial Intelligence — 96 citations into it
• Media Technology — 53 citations into it; #387 of 3,621 by trust here, #321 by citations
• Model Architectures — 29 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Aerospace Engineering — 10 citations into it
• Computational Mechanics — 6 citations into it

Whom David S. Doermann trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. A. Bovik
4. Kaiming He
5. Yann LeCun
6. Simon M. M. Lucas
7. Yoshua Bengio
8. Diederik P. Kingma
9. Adam Coates
10. Ross B. Girshick

Who trusts David S. Doermann overall

1. Jongyoo Kim
2. Qixiang Ye
3. Cong Yao
4. Kede Ma
5. Xiang Bai
6. Weixia Zhang
7. Feng Zhou
8. Yochai Blau
9. Yi-Feng Pan
10. Jiayi Ma

Papers from David S. Doermann's perspective

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

Their papers

• Convolutional Neural Networks for No-Reference Image Quality Assessment (2014) — 1,187 citations
• Text Detection and Recognition in Imagery: A Survey (2015) — 755 citations
• Automatic text detection and tracking in digital video (2000) — 662 citations
• Towards Optimal Structured CNN Pruning via Generative Adversarial Learning (2019) — 578 citations
• Camera-based analysis of text and documents: a survey (2005) — 485 citations
• Blind Image Quality Assessment Based on High Order Statistics Aggregation (2016) — 473 citations
• YOLOv12: Attention-Centric Real-Time Object Detectors (2025) — 1,926 citations
• Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (2019) — 354 citations
• Unsupervised feature learning framework for no-reference image quality assessment (2012) — 867 citations
• Robust point matching for nonrigid shapes by preserving local neighborhood structures (2006) — 313 citations