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English(EN) Geometry-based Schr\"odinger Bridges for Trustworthy Multimodal Fusion

新GMF方法提升多模态融合可信度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为基于几何的多模态融合（GMF）的新方法，以提高结合来自多个来源数据的系统的可信度。与依赖模型自身置信度的现有方法不同，GMF通过测量潜在空间中所需的修正来评估数据可靠性。该方法使用扩散薛定谔桥传输来量化输入数据所需的调整量，即使模型自信地出错也能标记不可靠的输入。实验表明，与传统的基于置信度的基线相比，GMF在对抗传感器噪声和冲突数据方面显著增强了鲁棒性。 AI

影响 增强了处理多个数据流的AI系统的可靠性，这对于现实世界的应用至关重要。

排序理由 这是一篇描述多模态融合新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新GMF方法提升多模态融合可信度

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiayu Xiong, Jing Wang, Qi Zhang, Wanlong Wang, Jun Xue ·

    基于几何的Schr\"odinger桥用于可信多模态融合

    arXiv:2605.31193v1 Announce Type: new Abstract: Real-world multimodal systems must be robust against low-quality data, such as sensor noise, incomplete multimodal data and conflicting inputs. However, existing trustworthy fusion methods rely on the model's own prediction confiden…

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