Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
Hahyeon Choi, Nojun Kwak
Why It Matters
What makes this one worth your time
This framework provides a practical alternative to existing methods in multimodal learning, potentially enhancing performance and efficiency in various applications.
S3 offers a novel structural approach to multimodal learning by leveraging semantic experts and selective routing.
Summary
The paper introduces the S3 framework for multimodal learning, which decomposes inputs into semantic experts and selectively routes them based on task requirements, improving accuracy and demonstrating a reverse U-shaped sparsity-performance trend across benchmarks.
Key contributions
- Introduction of the S3 framework for multimodal learning.
- Demonstration of improved accuracy across multiple benchmarks.
- Identification of a reverse U-shaped sparsity-performance relationship.
Notable insights
- The reverse U-shaped sparsity-performance trend suggests that intermediate sparsity may optimize model performance, challenging conventional beliefs about sparsity.
- Decomposing multimodal inputs into semantic experts allows for more tailored and efficient processing of diverse data types.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.03348v2 Announce Type: cross 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.