  Metaphors are a common communication tool used in our day-to-day life. The
detection and generation of metaphors in textual form have been studied
extensively but metaphors in other forms have been under-explored. Recent
studies have shown that Vision-Language (VL) models cannot understand visual
metaphors in memes and adverts. As of now, no probing studies have been done
that involve complex language phenomena like metaphors with videos. Hence, we
introduce a new VL task of describing the metaphors present in the videos in
our work. To facilitate this novel task, we construct and release a manually
created dataset with 705 videos and 2115 human-written captions, along with a
new metric called Average Concept Distance (ACD), to automatically evaluate the
creativity of the metaphors generated. We also propose a novel low-resource
video metaphor captioning system: GIT-LLaVA, which obtains comparable
performance to SoTA video language models on the proposed task. We perform a
comprehensive analysis of existing video language models on this task and
publish our dataset, models, and benchmark results to enable further research.
