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English(EN) Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration

新的CPSC框架改进了低质量数据上的多模态学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了一个名为共形预测自校准（CPSC）的新框架，以应对多模态学习中的挑战，特别是处理以模态不平衡和噪声腐蚀为特征的低质量数据。CPSC集成了自校准训练循环以及表示和梯度自校准模块，利用共形预测来评估实例的可靠性并指导学习过程。在六个数据集上的实验表明，CPSC在不平衡和噪声场景中均优于现有方法。 AI

影响 引入了一种提高多模态学习鲁棒性的新方法，有可能在具有不完美数据的实际应用中提高性能。

排序理由 这是一篇详细介绍多模态学习新框架的研究论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的CPSC框架改进了低质量数据上的多模态学习

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xing Xu ·

    在低质量数据上进行多模态学习，并带有共形预测自校准

    Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they share a common root in the predictive uncertainty…

  2. arXiv cs.CV TIER_1 English(EN) · Xun Jiang, Yufan Gu, Disen Hu, Yuqing Hou, Yazhou Yao, Fumin Shen, Heng Tao Shen, Xing Xu ·

    在低质量数据上进行多模态学习，并进行共形预测自校准

    arXiv:2605.03820v1 Announce Type: new Abstract: Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they s…

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