  In this work, we study music/video cross-modal recommendation, i.e.
recommending a music track for a video or vice versa. We rely on a
self-supervised learning paradigm to learn from a large amount of unlabelled
data. We rely on a self-supervised learning paradigm to learn from a large
amount of unlabelled data. More precisely, we jointly learn audio and video
embeddings by using their co-occurrence in music-video clips. In this work, we
build upon a recent video-music retrieval system (the VM-NET), which originally
relies on an audio representation obtained by a set of statistics computed over
handcrafted features. We demonstrate here that using audio representation
learning such as the audio embeddings provided by the pre-trained MuSimNet,
OpenL3, MusicCNN or by AudioSet, largely improves recommendations. We also
validate the use of the cross-modal triplet loss originally proposed in the
VM-NET compared to the binary cross-entropy loss commonly used in
self-supervised learning. We perform all our experiments using the Music Video
Dataset (MVD).
