Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts

2026-05-05

AI Summary

This work addresses the limitations of conventional multimodal approaches that rely on fixed embeddings and struggle to flexibly capture task-relevant semantic structures. The authors propose S3, a novel framework that unifies specialized semantic experts, task-adaptive routing, and sparsification mechanisms within a Mixture-of-Experts (MoE) architecture, enabling dynamic selection and pruning to form structured multimodal representations. Evaluated on four MultiBench benchmarks, S3 achieves substantial performance gains. Notably, the study reveals an inverted U-shaped relationship between model performance and sparsity: optimal results are attained at moderate sparsity levels, effectively balancing computational efficiency with representational capacity.

Abstract

We propose S3 (Specialization, Selection, Sparsification), a framework that rethinks multimodal learning through a structural perspective. Instead of encoding all signals into a fixed embedding, S3 decomposes multimodal inputs into semantic experts and selectively routes them for each task. Specialization forms concept-level experts in a shared latent space, Selection adapts routing for task-specific needs, and Sparsification prunes low-utility paths to yield compact, information-minimal representations. Across four MultiBench benchmarks, S3 improves accuracy and shows a consistent reverse U-shaped sparsity-performance trend, with peak performance at intermediate sparsity. These results suggest that structuring multimodal representations as selectable semantic components provides a practical and principled alternative to contrastive learning or InfoMax-driven approaches.

Problem

• multimodal representations
• semantic structure
• task adaptation
• representation sparsity
• multi-modal learning

Innovation

• Mixture-of-Experts
• Multimodal Learning
• Structural Representation
• Sparsification
• Task-specific Routing