metadata, such as title, description, and tags, and they are labeled as either flood (1) or non-flood (0). The task predefines a development set (devset) of samples, and a test set of samples, as well as its own evaluation protocol.

-   **Brodatz** [@brodatz:1966] is a dataset of texture images, labeled across $111$ classes. There are $16$ samples per class, composing a total of samples.

-   **Soccer** [@van:2006:soccer] is an image dataset, labeled across 7 categories (soccer teams), containing $40$ images each.

-   **UW** [@Deselaers:2008:UW], also called University of Washington dataset, is a multimodal collection of images annotated by textual keywords. The images are pictures labeled across $22$ classes (locations). Pictures per class vary from 22 to 255. The number of keywords per picture vary from 1 to 22.

Evaluation Protocol
-------------------

For any dataset that does not explicitly define train and test sets, we initially split it in train and test sets, at a proportion of 80% and 20% respectively, in a stratified way so that the proportions per class remain equal. The same train and test sets per dataset are adopted to evaluate all methods under the same circumstances, as well as the evaluation metrics.

For each representation model, 