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English(EN) FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification

FedSSMCoOp框架为生物医学图像分类实现轻量级联邦学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了FedSSMCoOp，一个新颖的联邦学习框架，专为少样本图像分类设计，特别是在数据隐私至关重要的生物医学应用中。该框架利用基于SSM的Vision Mamba和Cross Mamba块，无需外部大型语言模型进行特征对齐即可实现多模态学习。通过仅优化软提示和通信提示的更新，FedSSMCoOp与现有基线相比，实现了1.96倍更轻的占用空间，同时在各种生物医学图像数据集上保持了稳定的性能。 AI

影响 该框架可以实现有限数据专业领域中更高效、更注重隐私的AI模型训练。

排序理由 该集群包含一篇详细介绍联邦学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

FedSSMCoOp框架为生物医学图像分类实现轻量级联邦学习

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该集群包含一篇详细介绍联邦学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ankita Das, Ambarish Parthasarathy, Sumohana S. Channappayya, C. Krishna Mohan ·

    FedSSMCoOp：用于轻量级联邦提示学习的SSM编码器，用于少样本分类

    arXiv:2610.09907v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information contained in the respective domains. Despite the performance gains, most of these ap…

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