Convex combinations of attention-weighted face and voice embeddings, trained with clustering and multi-similarity loss, improve cross-modal person association on VoxCeleb.
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
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abstract
We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods, DIMNet does not explicitly learn the joint relationship between the modalities. Instead, DIMNet learns a shared representation for different modalities by mapping them individually to their common covariates. These shared representations can then be used to find the correspondences between the modalities. We show empirically that DIMNet is able to achieve better performance than other current methods, with the additional benefits of being conceptually simpler and less data-intensive.
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cs.CV 1years
2026 1verdicts
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Face and Voice Cross-modal Association with Learning Convex Feature Embedding
Convex combinations of attention-weighted face and voice embeddings, trained with clustering and multi-similarity loss, improve cross-modal person association on VoxCeleb.