one often focuses on a specific point of view of a single modality. For example, dedicated descriptors may be created to characterize scenes, textual descriptions, movement, symbols, signals, etc. For this reason, descriptors and retrieval models often provide complementary views, when adopted in combination.

Many research works have been proposed to combine heterogeneous data sources (remotely sensed information and social media) to promote multimodal analysis. In [@zhou:2017:CBIR], authors point out the benefit of exploring and fusing multimodal features with different models. Moreover, combining different kinds of features (local vs holistic) improves substantially the retrieval effectiveness [@zhang:2015:queryRankFusion], and most search fusion approaches are based on rank fusion [@dao:2018; @Dourado:2019:FG; @zhang:2015:queryRankFusion].

Scenarios involving heterogeneous data impose a challenge of selecting features or models to combine, which is performed by either supervised or unsupervised approaches. Unsupervised approaches are necessary in the absence of labeled data, which is prominent nowadays, or in scenarios involving lower computation capabilities or large amount of data.

Most previous initiatives for multimodal prediction are solely based on either CNN-based descriptors in isolation, feature concatenation [@dirsm17:BBischkeEtAl; @dirsm17:MSDaoEtAl; @dirsm17:XFuEtAl; @dirsm17:MHanifEtAl; @dirsm17:LLopezFuentesEtAl; @dirsm17:NTkachenkoEtAl], or graph-based feature-fusion [@dirsm17:KAvgerinakisEtAl; @werneck:2018:BocgBokg]. These approaches still ignore the correlation between modalities, as well as object correlation, and 