We adopt SOFT assignment and AVG pooling, as suggested by the authors [@Dourado:2019:FV]. has been successfully applied in scenarios involving graph classification, textual representation and information retrieval [@Silva:2018:BoG; @Dourado:2019:BoTG; @Dourado:2019:FV]. To the best of our knowledge, this is the first extension of in the context of multimodal representation.

We refer to FV-V as the fusion vector generated by ${\mathcal{E}}_V$, while FV-H is generated by ${\mathcal{E}}_H$, and FV-K by ${\mathcal{E}}_K$.

Prediction based on Fusion Vectors {#sec:fusionVectorSearch}
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Fusion vectors allow the creation of predictors, such as classifiers or regressors, and also ad-hoc retrieval systems, depending on the underlying demanded task. In this work, we adopt them to build predictors, where training objects – associated with ground-truth information – are used to train an estimator for a certain input object be considered a label (or event) or not.

Let $S$ be a training corpus of size $n$. A predictor can be modeled as $f (X, \beta) \approx Y$, where $f$ is an approximation function, $X$ are the independent variables, $Y$ is the dependent variable (target), and $\beta$ are unknown parameters. A learning model explores $S$ to find a $f$ that minimizes a certain error metric. The training samples are generally labeled, so 