more investigation, specially for multimodal representation models. We explore how unsupervised rank aggregation capabilities can be applied to prediction tasks. Despite their original intent regarding better retrieval accuracy, we claim that unsupervised rank aggregation functions can provide an effective dataset exploitation.

Considering previous works regarding fusion approaches for prediction tasks, a considerable amount of them are still based on classic visual descriptors [@dirsm17:MSDaoEtAl; @dirsm17:MHanifEtAl; @dirsm17:NTkachenkoEtAl; @werneck:2018:BocgBokg]. Most of them resorted to pre-trained CNN-based models for visual feature extraction [@dirsm17:KAhmadEtAl; @dirsm17:SAhmadEtAl; @dirsm17:KAvgerinakisEtAl; @dirsm17:BBischkeEtAl; @dirsm17:XFuEtAl; @dirsm17:LLopezFuentesEtAl; @dirsm17:KNogueiraEtAl], from which just a few fine-tuned their models [@dirsm17:LLopezFuentesEtAl; @dirsm17:KNogueiraEtAl]. When dealing with specific tasks, specially for competitions, some works use preprocessing steps, such as image cropping and filtering [@dirsm17:ZZhao], but this is beyond our general intent in this paper, regarding fusion methods and representation models.

In order to explore the textual modality, most initiatives used BoW, using either TF or TF-IDF weighting, while others presented more complex formulations, such as word embeddings [@dirsm17:BBischkeEtAl; @dirsm17:NTkachenkoEtAl], Long Short-Term Memory (LSTM) networks [@dirsm17:LLopezFuentesEtAl], or relation networks [@dirsm17:KNogueiraEtAl].

Regarding multimodal scenarios, most works rely on early-fusion approaches, such as a concatenation of visual and textual feature vectors [@dirsm17:BBischkeEtAl; @dirsm17:MSDaoEtAl; @dirsm17:XFuEtAl; @dirsm17:MHanifEtAl; @dirsm17:LLopezFuentesEtAl; @dirsm17:NTkachenkoEtAl] or graph-based attribute fusion [@dirsm17:KAvgerinakisEtAl; 