  In this paper, we study multimodal coreference resolution, specifically where
a longer descriptive text, i.e., a narration is paired with an image. This
poses significant challenges due to fine-grained image-text alignment, inherent
ambiguity present in narrative language, and unavailability of large annotated
training sets. To tackle these challenges, we present a data efficient
semi-supervised approach that utilizes image-narration pairs to resolve
coreferences and narrative grounding in a multimodal context. Our approach
incorporates losses for both labeled and unlabeled data within a cross-modal
framework. Our evaluation shows that the proposed approach outperforms strong
baselines both quantitatively and qualitatively, for the tasks of coreference
resolution and narrative grounding.
