Asymmetric Hierarchical Anchoring for Audio-Visual Joint Representation: Resolving Information Allocation Ambiguity for Robust Cross-Modal Generalization

• URL: http://arxiv.org/abs/2602.03570v1
• Date: Tue, 03 Feb 2026 14:14:03 GMT
• Title: Asymmetric Hierarchical Anchoring for Audio-Visual Joint Representation: Resolving Information Allocation Ambiguity for Robust Cross-Modal Generalization
• Authors: Bixing Wu, Yuhong Zhao, Zongli Ye, Jiachen Lian, Xiangyu Yue, Gopala Anumanchipalli,
• Abstract summary: We propose Asymmetric Hierarchical Anchoring (AHA) to enforce directional information allocation.<n>We replace fragile mutual information estimators with a GRL-based adversarial decoupler that explicitly suppresses semantic leakage.<n>AHA consistently outperforms symmetric baselines in cross-modal transfer.
• Score: 19.721857318111734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-visual joint representation learning under Cross-Modal Generalization (CMG) aims to transfer knowledge from a labeled source modality to an unlabeled target modality through a unified discrete representation space. Existing symmetric frameworks often suffer from information allocation ambiguity, where the absence of structural inductive bias leads to semantic-specific leakage across modalities. We propose Asymmetric Hierarchical Anchoring (AHA), which enforces directional information allocation by designating a structured semantic anchor within a shared hierarchy. In our instantiation, we exploit the hierarchical discrete representations induced by audio Residual Vector Quantization (RVQ) to guide video feature distillation into a shared semantic space. To ensure representational purity, we replace fragile mutual information estimators with a GRL-based adversarial decoupler that explicitly suppresses semantic leakage in modality-specific branches, and introduce Local Sliding Alignment (LSA) to encourage fine-grained temporal alignment across modalities. Extensive experiments on AVE and AVVP benchmarks demonstrate that AHA consistently outperforms symmetric baselines in cross-modal transfer. Additional analyses on talking-face disentanglement experiment further validate that the learned representations exhibit improved semantic consistency and disentanglement, indicating the broader applicability of the proposed framework.

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