Abstract
How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. What is a good caption? We investigate this problem by focusing on image-caption ratings and by generating high quality datasets from human feedback with gamification. We validate the datasets by showing a higher level of inter-rater agreement, and by using them to train custom machine learning models to predict new ratings. Our approach outperforms previous metrics - the resulting datasets are more easily learned and are of higher quality than other currently available datasets for image-caption rating.
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Scott, A. T., Narins, L. D., Kulkarni, A., Castanon, M., Kao, B., Ihorn, S., … Yoon, I. (2023). Improved Image Caption Rating - Datasets, Game, and Model. In Conference on Human Factors in Computing Systems - Proceedings. Association for Computing Machinery. https://doi.org/10.1145/3544549.3585632
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