Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present. We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training. We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents. We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.
@article{arxiv.1904.07826,
title = {Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents},
author = {Jack Hessel and Lillian Lee and David Mimno},
journal= {arXiv preprint arXiv:1904.07826},
year = {2019}
}
Comments
Code and data available at http://www.cs.cornell.edu/~jhessel/multiretrieval/multiretrieval.html