Overview of the 2017 RedICA text-image matching (RICATIM) challenge

Abstract

This paper describes the design and analysis of results of the 2017 RedICA: Text-Image Matching (RICATIM) challenge. This academic competition faces the image labeling problem (assigning words to images) as one binary classification. Motivated by recent success of representation learning, we built a data set for binary classification in which each instance is the learned representation of a pair of an image and a word. Instances are labeled as positive, if the word is relevant for describing the content of the image and negative otherwise. Thus, participants of the challenge had to develop binary classification methods to distinguish between relevant and irrelevant text-image matchings. The challenge attracted 43 participants, that provided quite original and competitive solutions. The performance obtained by the top ranked participants was impressive, improving the performance of the baseline considerably. In this paper we describe the approached problem, the challenge design (including data and evaluation protocol), and provide an overview of the results achieved by participants.