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为弱监督的语义分割建模标签分布

Linshan Wu, Zhun Zhong, Jiayi Ma

IEEE transactions on pattern analysis and machine intelligence

|April 2, 2025

概括

本研究引入了一个适应高斯混合模型 (AGMM) 用于弱监督的语义细分 (WSSS). 通过模拟语义相关性,AGMM框架产生了更准确的伪标签,提高了细分模型的性能,并降低了注释成本.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
人工智能的人工智能

背景情况:

弱监督语义细分 (WSSS) 通过使用有限的注释数据,为完全监督的方法提供了具有成本效益的替代方案.
当前的WSSS方法往往忽略了不同伪标签之间的语义关系,这可能会限制性能.
利用特征空间的近距离和分销中心可以提高伪标签的信心和准确性.

研究的目的:

开发一个新的WSSS框架,模拟底层标签分布,并利用跨标签约束来改进伪标签生成.
引入一个适应高斯混合模型 (AGMM),利用高斯混合模型 (GMMs) 来捕获标签分发.
提高伪标签的准确性和可靠性,以便在语义细分任务中进行更有效的监督.

主要方法:

拟议的适应高斯混合模型 (AGMM) 框架模型标记使用高斯混合模型 (GMMs) 的分布.
它计算伪标记像素的特征分布中心,并根据这些中心的距离构建GMM.
在线预期最大化 (OEM) 算法和新型最大化损失用于自适应GMM优化.

主要成果:

AGMM框架有效地模拟标签分配,并产生高质量的伪标签,以进行可靠的监督.
它展示了处理各种弱标签类型的能力,包括图像级标签,点,涂,块和界限框.
在所有设置中,PASCAL,COCO,Cityscapes和ADE20K数据集的实验表明与最先进的方法相比,性能优越.

结论:

拟议的AGMM框架为弱监督的语义细分提供了统一和有效的方法.
通过模拟标签分布和执行跨标签约束,它显著提高了伪标签的质量.
该方法取得了最先进的结果,突出了其在语义细分中降低注释成本的潜力.

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