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By supporting multi-modal retrieval training and evaluation, image captioningdatasets have spurred remarkable progress on representation learning.Unfortunately, datasets have limited cross-modal associations: images are notpaired with other images, captions are only paired with other captions of thesame image, there are no negative associations and there are missing positivecross-modal associations. This undermines research into how inter-modalitylearning impacts intra-modality tasks. We address this gap with CrisscrossedCaptions (CxC), an extension of the MS-COCO dataset with human semanticsimilarity judgments for 267,095 intra- and inter-modality pairs. We reportbaseline results on CxC for strong existing unimodal and multimodal models. Wealso evaluate a multitask dual encoder trained on both image-caption andcaption-caption pairs that crucially demonstrates CxC's value for measuring theinfluence of intra- and inter-modality learning.

Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO