  Recent years have witnessed the rapid development of short videos, which
usually contain both visual and audio modalities. Background music is important
to the short videos, which can significantly influence the emotions of the
viewers. However, at present, the background music of short videos is generally
chosen by the video producer, and there is a lack of automatic music
recommendation methods for short videos. This paper introduces MVBind, an
innovative Music-Video embedding space Binding model for cross-modal retrieval.
MVBind operates as a self-supervised approach, acquiring inherent knowledge of
intermodal relationships directly from data, without the need of manual
annotations. Additionally, to compensate the lack of a corresponding
musical-visual pair dataset for short videos, we construct a dataset,
SVM-10K(Short Video with Music-10K), which mainly consists of meticulously
selected short videos. On this dataset, MVBind manifests significantly improved
performance compared to other baseline methods. The constructed dataset and
code will be released to facilitate future research.
