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arXiv AI By Qun Dai, Liangjian Wen, Jiang Duan, Yong Dai, Dongkai Wang, Maolin Wang, Mingjie Wang, Jianzhuang Liu, He Yan, Zhao Kang

HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling

Read the original on arXiv AI →

The paper introduces HRIL, a method for learning multimodal representations that explicitly models higher‑order interactions among modalities using a cross‑moment tensor and Tucker decomposition. It adds a synergy‑aware regularizer to preserve higher‑order coupling capacity, aiming to capture synergistic signals that only emerge from joint configurations of multiple modalities. Experiments on controlled synergy tasks and real‑world benchmarks show consistent improvements over existing multimodal contrastive methods, especially on tasks dominated by synergistic interactions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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