HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling

Key points

• Self-supervised multimodal representation learning has achieved remarkable success across diverse domains, yet capturing synergistic information remains challenging due to the complexity of cross-modal interactions.1
• Unlike the shared information across individual modalities, synergy arises when task-relevant signals emerge only from the joint configuration of multiple modalities and cannot be recovered from any modality in isolation.1
• This work focuses on how to preserve the information capacity for such synergistic signals in multimodal representations.1
• HRIL employs Tucker decomposition to obtain a core tensor, complemented by a synergy-aware regularizer that prevents energy concentration and preserves higher-order coupling capacity for synergistic information capture.1

Sources (1)

• [1]HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:39 PM

  Motivated by this insight, we propose Higher-order Representation and Information Learning (HRIL), which constructs an empirical cross-moment tensor over modality embeddings to represent multi-way interactions.

  Self-supervised multimodal representation learning has achieved remarkable success across diverse domains, yet capturing synergistic information remains challenging due to the complexity of cross-modal interactions.

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