  The paper presents our proposed solutions for the MediaEval 2020
Flood-Related Multimedia Task, which aims to analyze and detect flooding events
in multimedia content shared over Twitter. In total, we proposed four different
solutions including a multi-modal solution combining textual and visual
information for the mandatory run, and three single modal image and text-based
solutions as optional runs. In the multimodal method, we rely on a supervised
multimodal bitransformer model that combines textual and visual features in an
early fusion, achieving a micro F1-score of .859 on the development data set.
For the text-based flood events detection, we use a transformer network (i.e.,
pretrained Italian BERT model) achieving an F1-score of .853. For image-based
solutions, we employed multiple deep models, pre-trained on both, the ImageNet
and places data sets, individually and combined in an early fusion achieving
F1-scores of .816 and .805 on the development set, respectively.
