ZERO-SHOT OPEN SET OBJECT DETECTION IN MUSIC ALBUM COVERS

We applied zero-shot object detection to music album
cover art enabling a quantitative iconographic analysis of
visual Pop music culture. For this we constructed and
partly manually annotated a dataset of decades of USA
Billboard chart music. We first used automatic image cap-
tioning to yield candidate object classes. Next we input
these object classes and the album cover images to a pre-
trained zero-shot object classification model, allowing de-
tection of objects and their classes without any re-training.
We confirmed accuracy of our approach on a manually
labeled sub sample of our dataset. Our results give an
overview of what different objects are depicted on album
covers belonging to different music genres and types of
artists.