$Y$ may be categorical. Still, a regressor can be built, as $E(Y|X)=f(X, \beta)$, so that posterior probabilities are inferred in order to estimate a confidence of a sample to refer to a class of not. $X$, in our case, refers to the fusion vectors, acting as variables that describes the samples in terms of their multiple multimodal ranks.

Experimental Evaluation {#secExpEval}
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We present, in this section, the experimental protocol used to evaluate the method, and the results achieved comparatively to state-of-the-art baselines. We evaluate the effectiveness of our method as a representation model in prediction tasks. The focus is on validating our fusion method comparatively to the individual use of descriptors with no fusion, as well as to compare it to early-fusion and late-fusion approaches.

Evaluation Scenarios
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We evaluate the proposed method on multiple datasets, comprised of heterogeneous multimodal data, in order to assess its general applicability. Our experimental evaluation comprises the following scenarios:

-   **ME17-DIRSM** dataset, the acronym for MediaEval 2017 Disaster Image Retrieval from Social Media [@bischke:2017:meSatellite], is a multimodal dataset of a competition whose goal is to infer whether images and/or texts refer to flood events or not. The samples contain images along with textual 