Joint-Centric Dual Contrastive Alignment with Structure-Preserving and Information-Balanced Regularization

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, extended

Summary

HILBERT (HIerarchical Long-sequence Balanced Embedding with Reciprocal contrastive Training) is a new cross-attentive multimodal framework designed for learning document-level audio-text representations from long, segmented sequences, particularly in low-resource data environments. It uses frozen pre-trained speech (e.g., Whisper, HuBERT) and language (e.g., T5, RoBERTa) encoders to extract segment-level features, which are then aggregated into modality-specific and joint document representations via cross-modal attention and self-attentive pooling. To address dimensional imbalance and preserve modality-specific structure, HILBERT employs a reciprocal dual contrastive objective that aligns audio-to-joint and text-to-joint representations. Auxiliary regularizers, including a Centered Kernel Alignment (CKA) loss for structural consistency and a mutual information balancing loss, prevent single-modality dominance. For downstream tasks, HILBERT utilizes a Mixture-of-Experts (MoE) classifier over concatenated audio, text, and joint representations. Evaluations across various audio-text backbone combinations show HILBERT achieves superior performance on highly imbalanced multi-class settings, such as mental disorder prediction, with AUC scores of 66.75% for parent data and 67.33% for offspring data on the 'spectrum' task.

Key takeaway

For NLP Engineers developing multimodal systems for long-sequence audio-text data, HILBERT offers a robust framework to mitigate dimensional imbalance and improve representation quality. You should consider integrating its dual contrastive alignment, CKA, and mutual information balancing losses to ensure equitable modality contributions and structural preservation, especially in low-resource or highly imbalanced classification scenarios like mental health prediction. This approach can yield significant performance gains over generic alignment methods.

Key insights

HILBERT aligns audio-text representations for long sequences by balancing modality contributions and preserving structure.

Principles

• Anchor modalities to a fused joint space.
• Balance information flow to prevent modality dominance.
• Leverage frozen pre-trained encoders for efficiency.

Method

HILBERT segments long audio/text, encodes with frozen models, aggregates via cross-modal attention, and aligns representations using a dual contrastive objective with CKA and MI losses, then classifies with an MoE.

In practice

• Use CKA loss to preserve structural consistency.
• Apply MI loss to balance modality contributions.
• Employ MoE for adaptive downstream task learning.

Topics

• Dual Contrastive Learning
• Centered Kernel Alignment
• Mutual Information (MI) Loss
• Mixture-of-Experts
• Audio-Text Alignment

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• Joint-Centric Dual Contrastive Alignment with Structure-Preserving and Information-Balanced Regularization — HILBERT aligns audio-text representations using dual contrastive learning and regularization for long sequences in low-resource settings.
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• OVA-IB: One vs All Information Bottleneck for Multi-Modal Alignment — OVA-IB aligns arbitrary modalities by applying the Information Bottleneck principle through a One-vs-All view, optimizing for shared and compressing unique information.
• Closing the Gap Between Text and Speech Understanding in LLMs — Speech-adapted LLMs underperform text-based counterparts due to forgetting and cross-modal misalignment.
• Contrastive-Difference CKA Reveals Concept-Specific Structural Alignment Across Language Model Architectures — Different LLM architectures exhibit a geometric-functional universality dissociation, allowing functional transfer despite moderate geometric differences.
• MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment — MIC enhances multi-scale representations by aligning isotropic subspaces and mitigating redundancy through regularization and self-distillation.

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