  With the increased accessibility of web and online encyclopedias, the amount
of data to manage is constantly increasing. In Wikipedia, for example, there
are millions of pages written in multiple languages. These pages contain images
that often lack the textual context, remaining conceptually floating and
therefore harder to find and manage. In this work, we present the system we
designed for participating in the Wikipedia Image-Caption Matching challenge on
Kaggle, whose objective is to use data associated with images (URLs and visual
data) to find the correct caption among a large pool of available ones. A
system able to perform this task would improve the accessibility and
completeness of multimedia content on large online encyclopedias. Specifically,
we propose a cascade of two models, both powered by the recent Transformer
model, able to efficiently and effectively infer a relevance score between the
query image data and the captions. We verify through extensive experimentation
that the proposed two-model approach is an effective way to handle a large pool
of images and captions while maintaining bounded the overall computational
complexity at inference time. Our approach achieves remarkable results,
obtaining a normalized Discounted Cumulative Gain (nDCG) value of 0.53 on the
private leaderboard of the Kaggle challenge.
