TY  - CONF
AU  - Laura Lopez-Fuentes
AU  - Joost Van de Weijer
AU  - Marc Bolaños
AU  - Harald Skinnemoen
A2  - MediaEval
PY  - 2017//
TI  - Multi-modal Deep Learning Approach for Flood Detection
BT  - MediaEval Benchmarking Initiative for Multimedia Evaluation
N2  - 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 themethod 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.
L1  - http://refbase.cvc.uab.es/files/LWB2017a.pdf
N1  - LAMP; 600.084; 600.109; 600.120
ID  - Laura Lopez-Fuentes2017
ER  -