Cross-modal music retrieval remains a challenging task for current search engines. Existing engines match music tracks using coarse-granularity retrieval of metadata, like pre-defined tags and genres. These methods face difficulties handling fine-granularity contextual queries. We propose a novel dataset of 66,048 image-music pairs for cross-modal music retrieval and introduce a hybrid-granularity retrieval framework using contrastive learning. Our method outperforms previous approaches, ensuring superior image-music alignment.
