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多语言环境下的脸-声关联（FAME）2024挑战赛评估计划

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

University of Engineering and Technology Taxila（拉合尔工程技术大学塔克西拉分校）；Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）；Fortemedia Singapore（Fortemedia新加坡公司）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Muhammad Saad Saeed, Shah Nawaz, Muhammad Salman Tahir, Rohan Kumar Das, Muhammad Zaigham Zaheer, Marta Moscati, Markus Schedl, Muhammad Haris Khan, Karthik Nan… 展开作者

Muhammad Saad Saeed, Shah Nawaz, Muhammad Salman Tahir, Rohan Kumar Das, Muhammad Zaigham Zaheer, Marta Moscati, Markus Schedl, Muhammad Haris Khan, Karthik Nandakumar, Muhammad Haroon Yousaf

备注：
ACM Multimedia Conference - Grand Challenge
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AI总结：

该挑战赛旨在多语言场景下探索人脸与声音的关联，使用MAV-Celeb数据集，并提供基线及任务细节，以推动多模态研究。

摘要：

技术的进步促进了多模态系统在各种实际应用中的使用。其中，音视频系统是广泛使用的多模态系统之一。近年来，由于人脸与声音之间存在独特的关联，人脸与声音的关联性研究受到了关注。多语言环境下的脸-声关联（FAME）2024挑战赛专注于在多语言场景这一独特条件下探索人脸与声音的关联。这一条件源于全球一半人口是双语者，且人们经常在多语言场景下交流的事实。该挑战赛使用一个名为多语言音视频（MAV-Celeb）的数据集来探索多语言环境下的人脸-声音关联。本报告提供了FAME挑战赛的详细信息，包括挑战赛介绍、数据集、基线和任务细节。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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