RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association
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Abstract (arXiv)
Face-voice association in multilingual environment challenge 2026 aims to investigate the face-voice association task in multilingual scenario. The challenge introduces English-German face-voice pairs to be utilized in the evaluation phase. To this end, we revisit the fusion and orthogonal projection for face-voice association by effectively focusing on the relevant semantic information within the two modalities. Our method performs favorably on the English-German data split and ranked 3rd in the FAME 2026 challenge by achieving the EER of 33.1.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 2 Dec 2025.
Builds on
9 of 12 references.
References · 9
- 2018Seeing Voices and Hearing Faces: Cross-modal biometric matchingcited by 60
- 2018Learnable PINs: Cross-Modal Embeddings for Person Identitycited by 60
- 2021Fusion and Orthogonal Projection for Improved Face-Voice Associationcited by 29
- 2020Cross-modal Speaker Verification and Recognition: A Multilingual Perspectivecited by 26
- 2019Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signalscited by 20
- 2023Single-branch Network for Multimodal Trainingcited by 13
- 2023Speaker Recognition in Realistic Scenario Using Multimodal Datacited by 9
- 2025PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Associationcited by 9
- 2025Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plancited by 6
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Citing papers per year: 2025: 1.
1 paper.