ID:
publications-1959
Type:
Peer reviewed articles
Year:
2023
Authors:
Thomas Papadimos, Stelios Andreadis, Ilias Gialampoukidis , Stefanos Vrochidis and Ioannis Kompatsiaris
Title:
Flood-Related Multimedia Benchmark Evaluation: Challenges, Results and a Novel GNN Approach
Venue/Journal:
Sensors MDPI
DOI:
10.3390/s23073767
Research type:
Simulation & Modeling
Water System:
Wastewater Treatment Plants
Technical Focus:
Abstract:
This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data.
Link with Projects:
101004157
Link with Tools:
Related policies:
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