  Contextualizing language technologies beyond a single language kindled
embracing multiple modalities and languages. Individually, each of these
directions undoubtedly proliferated into several NLP tasks. Despite this
momentum, most of the multimodal research is primarily centered around English
and multilingual research is primarily centered around contexts from text
modality. Challenging this conventional setup, researchers studied the
unification of multilingual and multimodal (MultiX) streams. The main goal of
this work is to catalogue and characterize these works by charting out the
categories of tasks, datasets and methods to address MultiX scenarios. To this
end, we review the languages studied, gold or silver data with parallel
annotations, and understand how these modalities and languages interact in
modeling. We present an account of the modeling approaches along with their
strengths and weaknesses to better understand what scenarios they can be used
reliably. Following this, we present the high-level trends in the overall
paradigm of the field. Finally, we conclude by presenting a road map of
challenges and promising research directions.
