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One of the essential human skills is the ability to seamlessly build an innerrepresentation of the world. By exploiting this representation, humans arecapable of easily finding consensus between visual, auditory and linguisticperspectives. In this work, we set out to understand and emulate this abilitythrough an explicit representation for both vision and language - Graphs ofEvents in Space and Time (GEST). GEST alows us to measure the similaritybetween texts and videos in a semantic and fully explainable way, through graphmatching. It also allows us to generate text and videos from a commonrepresentation that provides a well understood content. In this work we showthat the graph matching similarity metrics based on GEST outperform classicaltext generation metrics and can also boost the performance of state of art,heavily trained metrics.

GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language