Damian Borth

Computer Vision and Pattern Recognition · with Dengel · 2013–2021

A researcher in the Science corpus: 15 papers; trusts 587 researchers and is trusted by 254 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 144 citations into it; #2,475 of 21,575 by trust here, #2,902 by citations
• Artificial Intelligence — 90 citations into it; #34,333 of 35,345 by trust here, #16,771 by citations
• Media Technology — 40 citations into it; #1,739 of 3,621 by trust here, #398 by citations
• Signal Processing — 30 citations into it
• Evaluation & Benchmarks — 28 citations into it; #1,429 of 3,147 by trust here, #711 by citations
• Model Architectures — 24 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Speech & Audio — 12 citations into it

Whom Damian Borth trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Karen Simonyan
5. Ilya Sutskever
6. Yann LeCun
7. Yoshua Bengio
8. Jia Deng
9. Diederik P. Kingma
10. Andrew Zisserman

Who trusts Damian Borth overall

1. Dr. Hiteshwar Kumar Azad
2. Mohammad Soleymani
3. Aditya Khosla
4. Robert Rietveld
5. Quanzeng You
6. Tao Chen
7. Benjamin Bischke
8. Sicheng Zhao
9. Sergey Karayev
10. Trevor Darrell

Papers from Damian Borth's perspective

Ordered by how strongly the researchers Damian Borth 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• The New Data and New Challenges in Multimedia Research (2015) — 332 citations
• EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2017) — 2,804 citations
• Large-scale visual sentiment ontology and detectors using adjective noun pairs (2013) — 850 citations
• Large-Scale Deep Learning on the YFCC100M Dataset (2015) — 32 citations
• Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2018) — 293 citations
• Real-time Analysis and Visualization of the YFCC100m Dataset (2015) — 48 citations
• The YLI-MED Corpus: Characteristics, Procedures, and Plans (2015) — 25 citations
• NoiseVC: Towards High Quality Zero-Shot Voice Conversion (2021) — 6 citations
• Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement (2020) — 11 citations
• Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks (2017) — 60 citations
• Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks (2017) — 272 citations
• Contextual Enrichment of Remote-Sensed Events with Social Media Streams (2016) — 41 citations
• YFCC100M (2015) — 995 citations
• The Placing Task: A Large-Scale Geo-Estimation Challenge for Social-Media Videos and Images (2014) — 41 citations
• DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks (2014) — 314 citations