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	Author	Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen
	Title	Multi-modal Deep Learning Approach for Flood Detection			Type	Conference Article
	Year	2017	Publication	MediaEval Benchmarking Initiative for Multimedia Evaluation	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task.
	Address	Dublin; Ireland; September 2017
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	MediaEval
	Notes	LAMP; 600.084; 600.109; 600.120			Approved	no
	Call Number	Admin @ si @ LWB2017a			Serial	2974
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