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Improved Image Caption Rating - Datasets, Game, and Model
Andrew Taylor Scott1, Lothar D Narins1, Anagha Kulkarni1
1Department of Computer Science, San Francisco State University, San Francisco, CA, USA.
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.

