June 10, 2024, 4:46 a.m. | Bing Cao, Yinan Xia, Yi Ding, Changqing Zhang, Qinghua Hu

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04802v1 Announce Type: cross
Abstract: Multimodal fusion is crucial in joint decision-making systems for rendering holistic judgments. Since multimodal data changes in open environments, dynamic fusion has emerged and achieved remarkable progress in numerous applications. However, most existing dynamic multimodal fusion methods lack theoretical guarantees and easily fall into suboptimal problems, yielding unreliability and instability. To address this issue, we propose a Predictive Dynamic Fusion (PDF) framework for multimodal learning. We proceed to reveal the multimodal fusion from a generalization …

arxiv cs.cv cs.lg dynamic fusion predictive type

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