PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Association

2025-05-22

AI Summary

To address challenges in face-voice cross-modal matching—including reliance on hand-crafted negative samples, sensitivity to distant-margin hyperparameters, and embedding-space heterogeneity hindering alignment—this paper proposes a co-optimized joint embedding framework. Methodologically, it introduces: (1) a differentiable similarity calibration mechanism for precise alignment between face and voice embedding spaces; (2) an enhanced gated fusion module that explicitly enforces orthogonality constraints in the joint embedding to mitigate modality-specific structural biases; and (3) an end-to-end contrastive learning framework eliminating manual negative sample mining. Evaluated on VoxCeleb, the method achieves significantly higher face-voice matching accuracy than state-of-the-art approaches. Results demonstrate that the synergistic design of space alignment and fusion mechanisms not only improves performance but also enhances generalizability across diverse speaker identities and acoustic conditions.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

We study the task of learning association between faces and voices, which is gaining interest in the multimodal community lately. These methods suffer from the deliberate crafting of negative mining procedures as well as the reliance on the distant margin parameter. These issues are addressed by learning a joint embedding space in which orthogonality constraints are applied to the fused embeddings of faces and voices. However, embedding spaces of faces and voices possess different characteristics and require spaces to be aligned before fusing them. To this end, we propose a method that accurately aligns the embedding spaces and fuses them with an enhanced gated fusion thereby improving the performance of face-voice association. Extensive experiments on the VoxCeleb dataset reveals the merits of the proposed approach.

Problem

• Learning face-voice association without negative mining
• Aligning embedding spaces of faces and voices
• Improving fusion with enhanced gated feature fusion

Innovation

• Precise alignment of face-voice embedding spaces
• Enhanced gated fusion for feature integration
• Orthogonality constraints in joint embedding space