Delayed Commitment for Representation Readiness in Stage-wise Audio-Visual Learning

2026-05-02

AI Summary

This work addresses the problem of “perceptual premature commitment” in staged audio-visual learning, where early fusion of underprepared representations degrades downstream performance. To mitigate this, the authors propose the Delayed Perceptual Commitment Network (DPC-Net), which introduces the novel concept of representation readiness. DPC-Net employs observable proxy metrics to assess the preparedness of intermediate representations, identifies intervention-sensitive bottlenecks, and performs perceptual correction by integrating cross-layer and cross-modal evidence. The method establishes a plug-in encoder-level intervention framework that requires no modifications to task heads or loss functions, ensuring compatibility with diverse audio-visual architectures. Consistent performance gains across speech separation, event localization, and speech recognition tasks demonstrate the broad effectiveness of readiness-guided correction in reconstruction, localization, and recognition scenarios.

Abstract

Stage-wise audio-visual encoders propagate fused intermediate states across layers, making the formation of later representations depend on the readiness of earlier fusion states. Strong local audio-visual agreement provides useful correspondence evidence, yet a fused state also needs sufficient cross-layer and cross-modal support before it can reliably guide later fusion. This paper studies this issue through propagation-aware representation readiness and formulates premature perceptual commitment as a readiness-deficiency problem, where local plausibility, propagation influence, and support insufficiency jointly appear at an intermediate stage. We propose the Delayed Perceptual Commitment Network (DPC-Net), an encoder-level framework that estimates an observable readiness-deficiency surrogate, localizes the intervention-sensitive bottleneck, and applies support-aware correction with cross-layer and cross-modal evidence. DPC-Net preserves task-specific heads, losses, decoding modules, and evaluation protocols, making it applicable to different audio-visual tasks through encoder-side intervention. Experiments on audio-visual speech separation, audio-visual event localization, and audio-visual speech recognition show consistent improvements across reconstruction, localization, and recognition regimes. Further analyses on component contribution, selection criteria, counterfactual intervention, and readiness trajectories support the effectiveness of readiness-guided bottleneck correction.

Problem

• representation readiness
• premature perceptual commitment
• audio-visual fusion
• stage-wise learning
• readiness deficiency

Innovation

• Delayed Perceptual Commitment
• Representation Readiness
• Cross-modal Fusion
• Stage-wise Learning
• Bottleneck Correction