Synergistic multimodal information requires explicit higher-order tensor modeling rather than standard pairwise interactions; HRIL's approach consistently outperforms existing methods on tasks where modalities must work together.
This paper addresses how to capture synergistic information in multimodal learning—signals that only emerge from combining multiple modalities together, not from any single modality alone. The authors propose HRIL, which uses higher-order tensor mathematics (specifically Tucker decomposition) to model complex cross-modal interactions and preserve the capacity to learn these synergistic signals.