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.
@article{arxiv.2310.13619,
title = {Semi-supervised multimodal coreference resolution in image narrations},
author = {Arushi Goel and Basura Fernando and Frank Keller and Hakan Bilen},
journal= {arXiv preprint arXiv:2310.13619},
year = {2023}
}