Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Multimodal AI · Depth: Expert, extended

Summary

The Hierarchical Semantic-Constrained Heterogeneous Graph (HSCHG) framework addresses challenges in open-vocabulary audio-visual event localization (OV-AVEL), specifically the difficulty in maintaining audio-visual consistency for unseen categories and establishing semantic consistency across segment- and video-levels. HSCHG first constructs a heterogeneous hierarchical graph in Euclidean space, incorporating audio and visual segment nodes and video-level nodes. It uses multi-directional temporal edges and a dual-threshold filtering gated fusion strategy (with τ₁=0.2 and τ₂=0.5) to robustly integrate cross-modal information. Subsequently, the framework maps these multi-level audio-visual representations and text prototypes into hyperbolic space, employing a hierarchical entailment regularization loss to explicitly model hierarchical relationships. Extensive experiments demonstrate that HSCHG outperforms existing methods on the OV-AVEL benchmark, with ablation studies confirming its components' effectiveness.

Key takeaway

For AI Scientists and Machine Learning Engineers developing robust open-vocabulary video understanding systems, HSCHG offers a novel approach to improve event localization. You should consider integrating heterogeneous graph networks for multi-level temporal and cross-modal reasoning, especially when dealing with asynchronous signals. Leveraging hyperbolic space with hierarchical entailment constraints can significantly enhance generalization to unseen categories by better modeling semantic hierarchies.

Key insights

HSCHG improves open-vocabulary audio-visual event localization by combining Euclidean graph modeling with hyperbolic space for hierarchical semantic alignment.

Principles

• Hyperbolic space excels at representing hierarchical data structures.
• Dual-threshold filtering enhances cross-modal fusion robustness.
• Multi-level semantic constraints improve generalization.

Method

HSCHG builds a heterogeneous graph in Euclidean space with segment and video nodes, using multi-directional temporal edges and dual-threshold gated fusion. It then maps features to hyperbolic space, applying a hierarchical entailment loss.

In practice

• Apply dual-threshold filtering for noisy multi-modal data fusion.
• Consider hyperbolic embeddings for hierarchical data modeling.
• Use graph networks for complex temporal and cross-modal dependencies.

Topics

• Open-Vocabulary Event Localization
• Audio-Visual Learning
• Heterogeneous Graph Networks
• Hyperbolic Embeddings
• Multi-modal Representation Learning
• Temporal Reasoning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.