  We propose a content-based system for matching video and background music.
The system aims to address the challenges in music recommendation for new users
or new music give short-form videos. To this end, we propose a cross-modal
framework VMCML that finds a shared embedding space between video and music
representations. To ensure the embedding space can be effectively shared by
both representations, we leverage CosFace loss based on margin-based cosine
similarity loss. Furthermore, we establish a large-scale dataset called MSVD,
in which we provide 390 individual music and the corresponding matched 150,000
videos. We conduct extensive experiments on Youtube-8M and our MSVD datasets.
Our quantitative and qualitative results demonstrate the effectiveness of our
proposed framework and achieve state-of-the-art video and music matching
performance.
