  With the rise of short videos, the demand for selecting appropriate
background music (BGM) for a video has increased significantly, video-music
retrieval (VMR) task gradually draws much attention by research community. As
other cross-modal learning tasks, existing VMR approaches usually attempt to
measure the similarity between the video and music in the feature space.
However, they (1) neglect the inevitable label noise; (2) neglect to enhance
the ability to capture critical video clips. In this paper, we propose a novel
saliency-based self-training framework, which is termed SSVMR. Specifically, we
first explore to fully make use of the information containing in the training
dataset by applying a semi-supervised method to suppress the adverse impact of
label noise problem, where a self-training approach is adopted. In addition, we
propose to capture the saliency of the video by mixing two videos at span level
and preserving the locality of the two original videos. Inspired by back
translation in NLP, we also conduct back retrieval to obtain more training
data. Experimental results on MVD dataset show that our SSVMR achieves the
state-of-the-art performance by a large margin, obtaining a relative
improvement of 34.8% over the previous best model in terms of R@1.
