Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915518677516288 |
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| author | Moscati, Marta Abdullah, Ahmed Saeed, Muhammad Saad Nawaz, Shah Das, Rohan Kumar Zaheer, Muhammad Zaigham Mir, Junaid Yousaf, Muhammad Haroon Malik, Khalid Schedl, Markus |
| author_facet | Moscati, Marta Abdullah, Ahmed Saeed, Muhammad Saad Nawaz, Shah Das, Rohan Kumar Zaheer, Muhammad Zaigham Mir, Junaid Yousaf, Muhammad Haroon Malik, Khalid Schedl, Markus |
| contents | The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, audio-visual systems are among the most widely used multimodal systems. In the recent years, associating face and voice of a person has gained attention due to the presence of unique correlation between them. The Face-voice Association in Multilingual Environments (FAME) 2026 Challenge focuses on exploring face-voice association under the unique condition of a 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 scenarios. The challenge uses a dataset named Multilingual Audio-Visual (MAV-Celeb) for exploring face-voice association in multilingual environments. This report provides the details of the challenge, dataset, baseline models, and task details for the FAME Challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan Moscati, Marta Abdullah, Ahmed Saeed, Muhammad Saad Nawaz, Shah Das, Rohan Kumar Zaheer, Muhammad Zaigham Mir, Junaid Yousaf, Muhammad Haroon Malik, Khalid Schedl, Markus Computer Vision and Pattern Recognition The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, audio-visual systems are among the most widely used multimodal systems. In the recent years, associating face and voice of a person has gained attention due to the presence of unique correlation between them. The Face-voice Association in Multilingual Environments (FAME) 2026 Challenge focuses on exploring face-voice association under the unique condition of a 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 scenarios. The challenge uses a dataset named Multilingual Audio-Visual (MAV-Celeb) for exploring face-voice association in multilingual environments. This report provides the details of the challenge, dataset, baseline models, and task details for the FAME Challenge. |
| title | Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.04592 |