Such approaches have been employed in various tasks including sentiment (Wang et al., 2014) and emotion analysis (Poria et al., 2015;Wimmer et al., 2008) and benefit from the ability of the learning model to capture the semantic relations between different modalities; however, the resulting features are treated in the same way by the learning model (Akbari et al., 2015).
in late fusion approaches different models are trained per modality and their outputs are combined at a later stage, usually by employing weighted sum (Dobrišek et al., 2013;Poria et al., 2016).