Effective Modeling of Cross-Modal Complementarity

Determine how to effectively model the information complementarity between different modalities in multimodal fusion for robot vision systems, where heterogeneous inputs (e.g., visual, depth, LiDAR, radar, language, or tactile signals) exhibit disparate structures and distributions that hinder unified representation and alignment.

Background

The survey highlights heterogeneity as a central challenge in multimodal fusion because different modalities (such as images, text, and audio) possess distinct data structures and feature distributions. This heterogeneity complicates direct fusion, uniform representation learning, and information interaction.

Existing approaches include unified feature space learning, modality-specific encoders with cross-modal attention, and adaptive modality fusion. Despite progress, directly capturing and leveraging complementary information across modalities remains unresolved, motivating research into methods (e.g., graph neural networks and self-supervised learning) that can better model complex inter-modal relationships.