can be used. In the second scenario, called “textual”, only textual data are used. In the third scenario, called “multimodal”, both visual and textual data are expected to be used. The correctness is evaluated, over the test set, by the metric Average Precision at $K$ (AP@K) at various cutoffs (50, 100, 250, 480), and by their mean value (mAP).

Although the ME17-DIRSM task may be seen as a multimodal binary classification problem, the evaluation metrics require ranking-based solutions, or equivalently confidence-level regressors, so that the first positions are the most likely to refer to a flood event. For the estimator component in ME17-DIRSM, we adopt SVR, an L2-regularized logistic regression based on linear SVM in its dual form, with probabilistic output scores, and trained over the fusion vectors from devset. Probabilistic scores are used so that we can sort the test samples by confidence expectancy of being flood.

In ME17-DIRSM, our results are compared to those from state-of-the-art baselines. In Soccer, Brodatz, and UW, our results are compared to those from two major fusion approaches: concatenation, and majority vote. They cover baselines from both early-fusion and late-fusion families. For the concatenation procedure, besides the concatenation itself, we normalize the vectors 