Most recommender systems adopt collaborative filtering (CF) and provide recommendations
based on past collective interactions. Therefore, the performance of CF algorithms degrades …

Audio-visual matching is an essential task that measures the correlation between audio clips
and visual images. However, current methods rely solely on the joint embedding of global …

The human brain has the capability to associate the unknown person's voice and face by
leveraging their general relationship, referred to as" cross-modal speaker verification''. This …

Conversational engagement estimation is posed as a regression problem, entailing the
identification of the favorable attention and involvement of the participants in the …

Recommender systems (RS) traditionally leverage the large amount of user–item interaction
data. This exposes RS to a lower recommendation quality in cold-start scenarios, as well as …

The advancements of technology have led to the use of multimodal systems in various real-
world applications. Among them, the audio-visual systems are one of the widely used …

This study introduces the first single branch network designed to tackle a spectrum of
biometric matching scenarios, including unimodal, multimodal, cross-modal, and missing …

Cognitive science has well-established the correlation between faces and voices because
neuro-cognitive pathways of both information share the same structure. Recently, the task …

Multimodal networks have demonstrated remarkable performance improvements over their
unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion …

Existing audio-visual cross-modal matching methods focus on mitigating cross-modal
heterogeneity but ignore the impact of intra-class discrepancy of the same identity in …