English(EN) Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels

新的HopS方法改进了具有部分标签的视觉语言模型的提示学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-16 04:00

研究人员开发了一种名为整体最优标签选择（HopS）的新方法，用于在仅有部分标签可用时改进视觉语言模型的提示学习。HopS采用两种策略：一种基于最近邻及其softmax分数识别最可能标签的局部过滤器，以及一种使用最优传输将采样分布映射到候选标签分布的全局目标。在八个基准数据集上的实验表明，HopS在部分监督下始终能提高性能，优于现有方法，并为弱监督场景提供了实用的解决方案。 AI

影响增强了弱监督场景下视觉语言模型的提示学习能力，有望提高其对多样化数据集的适应性。

排序理由该集群包含一篇详细介绍提示学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍提示学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yaqi Zhao, Haoliang Sun, Yating Wang, Yongshun Gong, Yilong Yin · 2026-07-16 04:00

部分标签下鲁棒提示学习的整体最优标签选择

arXiv:2604.06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks. However, when only partial labels are available, its performance…

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报道来源 [1]

部分标签下鲁棒提示学习的整体最优标签选择

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