This paper presents the method proposed by team UTAOS for the Mediaeval 2017 challenge on Multi-media and Satellite. In the first task, we mainly rely on different Convolutional Neural Network (CNN) models combined with two different late fusion methods. We also utilize the additional information available in the form of meta-data. The average and mean over precision at different cut-offs for our best runs are 84.94% and 95.11%, respectively. For challenge two, we utilize a Generative Adversarial Network (GAN). The mean Intersection-over-Union (IoU) for our best run is 0.8315.
CNN and GAN based satellite and social media data fusion for disaster detection / Ahmad, K.; Konstantin, P.; Riegler, M.; Conci, N.; Holversen, P.. - 1984:(2017). (Intervento presentato al convegno 2017 Multimedia Benchmark Workshop, MediaEval 2017 tenutosi a irl nel 2017).
CNN and GAN based satellite and social media data fusion for disaster detection
Ahmad K.;Conci N.;
2017-01-01
Abstract
This paper presents the method proposed by team UTAOS for the Mediaeval 2017 challenge on Multi-media and Satellite. In the first task, we mainly rely on different Convolutional Neural Network (CNN) models combined with two different late fusion methods. We also utilize the additional information available in the form of meta-data. The average and mean over precision at different cut-offs for our best runs are 84.94% and 95.11%, respectively. For challenge two, we utilize a Generative Adversarial Network (GAN). The mean Intersection-over-Union (IoU) for our best run is 0.8315.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione