Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO
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Image captioning datasets have proven useful for multimodal representation learning, and a common evaluation paradigm based on multimodal retrieval has emerged. Unfortunately, datasets have only limited cross-modal associations: images are not paired with others, captions are only paired with others that describe the same image, there are no negative associations and there are missing positive cross-modal associations. This undermines retrieval evaluation and limits research into how inter-modality learning impacts intra-modality tasks. To address this gap, we create the Crisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for 247,315 intra- and inter-modality pairs. We provide baseline model performance results for both retrieval and correlations with human rankings, emphasizing both intra- and inter-modality learning.
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