To Align or Not to Align: Strategic Multimodal Representation Alignment for Optimal Performance

TL;DR

This paper investigates how explicit representation alignment in multimodal learning affects model performance, revealing that optimal alignment depends on data redundancy and modality-specific information structures.

Contribution

It introduces a controllable contrastive learning module to systematically study the effects of alignment strength on multimodal models, providing practical guidance for optimal alignment strategies.

Findings

Optimal alignment depends on data redundancy.

Explicit alignment can improve or hinder performance.

Guidelines for when to apply alignment are provided.

Abstract

Multimodal learning often relies on aligning representations across modalities to enable effective information integration, an approach traditionally assumed to be universally beneficial. However, prior research has primarily taken an observational approach, examining naturally occurring alignment in multimodal data and exploring its correlation with model performance, without systematically studying the direct effects of explicitly enforced alignment between representations of different modalities. In this work, we investigate how explicit alignment influences both model performance and representation alignment under different modality-specific information structures. Specifically, we introduce a controllable contrastive learning module that enables precise manipulation of alignment strength during training, allowing us to explore when explicit alignment improves or hinders performance. Our results on synthetic and real datasets under different data characteristics show that the impact of explicit alignment on the performance of unimodal models is related to the characteristics of the data: the optimal level of alignment depends on the amount of redundancy between the different modalities. We identify an optimal alignment strength that balances modality-specific signals and shared redundancy in the mixed information distributions. This work provides practical guidance on when and how explicit alignment should be applied to achieve optimal unimodal encoder performance.