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Damian Borth
Damian Borth
Professor of Artificial Intelligence & Machine Learning, University of St. Gallen
Adresă de e-mail confirmată pe unisg.ch - Pagina de pornire
Titlu
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YFCC100M: The New Data in Multimedia Research
B Thomee, B Elizalde, DA Shamma, K Ni, G Friedland, D Poland, D Borth, ...
Communications of the ACM 59 (2), 64-73, 2016
19832016
Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
P Helber, B Bischke, A Dengel, D Borth
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2019
16292019
Large-scale Visual Sentiment Ontology and Detectors using Adjective Noun Pairs
D Borth, R Ji, T Chen, T Breuel, SF Chang
ACM International Conference on Multimedia, 223-232, 2013
9202013
DeepSentibank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
T Chen, D Borth, T Darrell, SF Chang
arXiv preprint arXiv:1410.8586, 2014
4722014
The New Data and New Challenges in Multimedia Research
B Thomee, DA Shamma, G Friedland, B Elizalde, K Ni, D Poland, D Borth, ...
arXiv preprint arXiv:1503.01817, 2015
3312015
Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution
JP Bello, C Silva, O Nov, RL Dubois, A Arora, J Salamon, C Mydlarz, ...
Communications of the ACM 62 (2), 68-77, 2019
3252019
Multi-task Learning for Segmentation of Building Footprints with Deep Neural Networks
B Bischke, P Helber, J Folz, D Borth, A Dengel
2019 IEEE International Conference on Image Processing (ICIP), 1480-1484, 2019
3062019
Sentibank: Large-scale Ontology and Classifiers for Detecting Sentiment and Emotions in Visual Content
D Borth, T Chen, R Ji, SF Chang
Proceedings of the 21st ACM international conference on Multimedia, 459-460, 2013
2522013
Introducing EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification
P Helber, B Bischke, A Dengel, D Borth
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018
2132018
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
M Schreyer, T Sattarov, D Borth, A Dengel, B Reimer
arXiv preprint arXiv:1709.05254, 2017
1362017
The Multimedia Satellite Task at Mediaeval 2017
B Bischke, P Helber, C Schulze, S Venkat, A Dengel, D Borth
1102017
Self-Supervised Vision Transformers for Land-Cover Segmentation and Classification
L Scheibenreif, J Hanna, M Mommert, D Borth
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
852022
Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks.
B Bischke, P Bhardwaj, A Gautam, P Helber, D Borth, A Dengel
MediaEval, 2017
822017
Analysis and Forecasting of Trending Topics in Online Media Streams
T Althoff, D Borth, J Hees, A Dengel
Proceedings of the 21st ACM international conference on Multimedia, 907-916, 2013
662013
Keyframe Extraction for Video Tagging & Summarization.
D Borth, A Ulges, C Schulze, TM Breuel
Informatiktage 2008, 45-48, 2008
652008
Real-time Analysis and Visualization of the YFCC100M dataset
S Kalkowski, C Schulze, A Dengel, D Borth
Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining …, 2015
542015
The Placing Task: A Large-Scale GEO-Estimation Challenge for Social-Media Videos and Images
J Choi, B Thomee, G Friedland, L Cao, K Ni, D Borth, B Elizalde, ...
Proceedings of the 3rd ACM Multimedia Workshop on Geotagging and Its …, 2014
542014
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
K Schürholt, D Kostadinov, D Borth
Neural Information Processing Systems (NeurIPS), 2021
50*2021
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
M Schreyer, T Sattarov, C Schulze, B Reimer, D Borth
KDD 2019 Workshop on Anomaly Detection in Finance, 2019
472019
Contextual Enrichment of Remote-sensed Events with Social Media Streams
B Bischke, D Borth, C Schulze, A Dengel
Proceedings of the 24th ACM international conference on Multimedia, 1077-1081, 2016
472016
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