Computer Science > Multimedia
[Submitted on 15 Jul 2021 (v1), last revised 11 Dec 2022 (this version, v2)]
Title:Cross-modal Variational Auto-encoder for Content-based Micro-video Background Music Recommendation
View PDFAbstract:In this paper, we propose a cross-modal variational auto-encoder (CMVAE) for content-based micro-video background music recommendation. CMVAE is a hierarchical Bayesian generative model that matches relevant background music to a micro-video by projecting these two multimodal inputs into a shared low-dimensional latent space, where the alignment of two corresponding embeddings of a matched video-music pair is achieved by cross-generation. Moreover, the multimodal information is fused by the product-of-experts (PoE) principle, where the semantic information in visual and textual modalities of the micro-video are weighted according to their variance estimations such that the modality with a lower noise level is given more weights. Therefore, the micro-video latent variables contain less irrelevant information that results in a more robust model generalization. Furthermore, we establish a large-scale content-based micro-video background music recommendation dataset, TT-150k, composed of approximately 3,000 different background music clips associated to 150,000 micro-videos from different users. Extensive experiments on the established TT-150k dataset demonstrate the effectiveness of the proposed method. A qualitative assessment of CMVAE by visualizing some recommendation results is also included.
Submission history
From: Jing Yi [view email][v1] Thu, 15 Jul 2021 11:47:43 UTC (3,266 KB)
[v2] Sun, 11 Dec 2022 15:07:42 UTC (1,474 KB)
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