Explaining Automatic Image Assessment
This work addresses the need for more efficient and scalable explainability in aesthetic image assessment, though it appears incremental by building on prior methods.
The paper tackled the problem of explaining aesthetic assessment models by visualizing dataset trends and automatically categorizing visual aesthetic features using neural networks trained on different dataset versions, achieving results captured through existing and novel metrics.
Previous work in aesthetic categorization and explainability utilizes manual labeling and classification to explain aesthetic scores. These methods require a complex labeling process and are limited in size. Our proposed approach attempts to explain aesthetic assessment models through visualizing dataset trends and automatic categorization of visual aesthetic features through training neural networks on different versions of the same dataset. By evaluating the models adapted to each specific modality using existing and novel metrics, we can capture and visualize aesthetic features and trends.