RiVaT-Fuse: Reliability-Calibrated Variational Tensor Fusion for Multimodal Prediction under Modality Uncertainty

RiVaT‑Fuse introduces a reliability‑calibrated variational tensor fusion framework for multimodal image‑metadata prediction, treating fusion as a sample‑wise latent‑state estimation rather than simple aggregation. It replaces scalar modality confidence with matrix‑valued trust geometry, decomposes interactions into additive, multiplicative, and relational components, and couples the latent state with conditional robustness and structured multi‑task prediction. On an image‑level benchmark, RiVaT‑Fuse outperforms direct representation‑level baselines and improves probability and label stability under perturbation.

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 Machine Learning.

• multimodal
• benchmarks