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arxiv: 2404.09342 · v3 · pith:AYIGX3DTnew · submitted 2024-04-14 · 💻 cs.CV · cs.SD· eess.AS

Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan

classification 💻 cs.CV cs.SDeess.AS
keywords multilingualchallengeassociationface-voiceenvironmentsfamesystemsaudio-visual
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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 multimodal systems. In the recent years, associating face and voice of a person has gained attention due to presence of unique correlation between them. The Face-voice Association in Multilingual Environments (FAME) Challenge 2024 focuses on exploring face-voice association under a unique condition of multilingual scenario. This condition is inspired from the fact that half of the world's population is bilingual and most often people communicate under multilingual scenario. The challenge uses a dataset namely, Multilingual Audio-Visual (MAV-Celeb) for exploring face-voice association in multilingual environments. This report provides the details of the challenge, dataset, baselines and task details for the FAME Challenge.

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  1. AMR: Adaptive Modality Routing for Multimodal Polyglot Speaker Identification

    cs.LG 2026-06 unverdicted novelty 5.0

    AMR dynamically routes audio (W2V-BERT 2.0) and face (IResNet-18) embeddings via adapters and a KL-supervised router, reaching 99.07% average accuracy on POLY-SIM 2026 protocols and beating the FOP baseline by 32.73%.