in our case. For the remaining descriptors, we choose the Pearson correlation distance, defined as $1 - \rho(\mathbf{u}, \mathbf{v})$ (Equation \[eq:correlation\]), which is a general-purpose metric due to its suitability for highly dimensional data and scale invariance. $$\label{eq:Jaccard}
J(\mathbf{u},\mathbf{v}) = \frac{\sum_i \min(u_i,v_i)}{\sum_i \max(u_i,v_i)}$$ $$\label{eq:correlation}
\rho(\mathbf{u}, \mathbf{v}) = \frac{(\mathbf{u} - \bar{u}) \cdot (\mathbf{v} - \bar{v})}
{{\|(\mathbf{u} - \bar{u})\|}_2 {\|(\mathbf{v} - \bar{v})\|}_2}$$

The datasets, descriptors, and evaluation criteria, are summarized in Table \[tab:datasets\].

Fusion Setups
-------------

For both visual and textual scenarios in ME17-DIRSM, we analyze three variants of our method with respect to the input rankers for late-fusion. For the visual scenario, the combinations are ResNet50IN + NASNetIN, ResNet50IN + VGG16P365, and ResNet50IN + NASNetIN + VGG16P365. For the textual scenario, the combinations are BoW + 2grams, BoW + doc2vecWiki, and BoW + 2grams + doc2vecWiki.

As for the multimodal scenario, we investigate some combinations taking one ranker of each type, two of each, and three of each. Six multimodal combinations are evaluated: ResNet50IN + BoW, ResNet50IN + NASNetIN + BoW + 2grams, ResNet50IN + NASNetIN + BoW + doc2vecWiki, ResNet50IN + VGG16P365 + BoW + 2grams, ResNet50IN + VGG16P365 + BoW + doc2vecWiki, and ResNet50IN + NASNetIN + VGG16P365 + 