This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained transformers have led to significant improvements in retrieval effectiveness, existing research has relied on image-caption datasets that feature only simplistic image-text relationships and underspecified user models of retrieval tasks. To address the gap between these oversimplified settings and real-world applications for multimedia content creation, we introduce a new approach for building retrieval test collections. We leverage hierarchical structures and diverse domains of texts, styles, and types of images, as well as large-scale image-document associations embedded in Wikipedia. We formulate two tasks based on a realistic user model and validate our dataset through retrieval experiments using baseline models. AToMiC offers a testbed for scalable, diverse, and reproducible multimedia retrieval research. Finally, the dataset provides the basis for a dedicated track at the 2023 Text Retrieval Conference (TREC), and is publicly available at https://github.com/TREC-AToMiC/AToMiC.
@article{arxiv.2304.01961,
title = {AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation},
author = {Jheng-Hong Yang and Carlos Lassance and Rafael Sampaio de Rezende and Krishna Srinivasan and Miriam Redi and Stéphane Clinchant and Jimmy Lin},
journal= {arXiv preprint arXiv:2304.01961},
year = {2023}
}