# Towards Language-Independent Face-Voice Association with Multimodal Foundation Models

arXiv: https://arxiv.org/abs/2512.02759 · HTML: https://mlsanity.com/paper/2512.02759/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge](https://mlsanity.com/paper/2512.20376/) (2025)
- [Adaptive Multimodal Person Recognition: A Robust Framework for Handling Missing Modalities](https://mlsanity.com/paper/2512.14961/) (2025)

## Abstract (arXiv)

This paper describes the UZH-CL system submitted to the FAME2026 Challenge. The challenge focuses on cross-modal verification under unique multilingual conditions, specifically unseen and unheard languages. Our approach investigates two distinct architectures, consisting of a baseline dual-encoder system trained from scratch using contrastive and orthogonal projection losses, and a foundation model approach leveraging ImageBind with LoRA. To address the data scarcity and language constraints of the challenge, we curated an external Arabic dataset from VoxBlink. Our best-performing system, ImageBind-LoRA, demonstrates remarkable cross-lingual generalization: despite being fine-tuned exclusively on Arabic audio, it achieved an EER of 24.73% on the evaluation set (English and German), securing 2nd place in the competition.