Mining Contextualized Visual Associations from Images for Creativity Understanding

ACL ARR 2025 February Submission6936 Authors

Abstract: Understanding another person’s creative output requires a shared language of association. However, when training vision-language models such as CLIP, we rely on web-scraped datasets containing short, predominantly literal, alt-text. In this work, we introduce a method for mining contextualized associations for salient visual elements in an image that can scale to any unlabeled dataset. Given an image, we can use these mined associations to generate high quality creative captions at increasing degrees of abstraction. With our method, we produce a new dataset of visual associations and $1.7$m creative captions for the images in MSCOCO. Human evaluation confirms that these captions remain visually grounded while exhibiting recognizably increasing abstraction. Moreover, fine-tuning a visual encoder on this dataset yields meaningful improvements in zero-shot image-text retrieval in two creative domains: poetry and metaphor visualization. We release our dataset, our generation code and our models for use by the broader community.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: corpus creation, lexicon creation, cross-modal content generation

Contribution Types: Data resources

Languages Studied: English

Submission Number: 6936