分支相互促进端到端弱监督的语义细分
IEEE transactions on neural networks and learning systems
|October 14, 2024
概括
本研究引入了一种对端到端弱监督语义细分 (E2E-WSSS) 的新方法,通过使分类和细分分支行之间的平等贡献和相互促进,超越现有方法.
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:
- 端到端弱监督的语义细分 (E2E-WSSS) 训练模型仅使用图像级注释.
- 目前的E2E-WSSS方法通常有一个分类分支主导培训,限制分支之间的互助.
研究的目的:
- 开发一种新的E2E-WSSS方法,以同等方式对待分类和细分分支.
- 通过双向监督和互动操作,加强两支机构之间的相互促进.
主要方法:
- 提出了一种双向监管机制,以确保分类和细分输出之间的一致性.
- 引入了互动操作,以便在两个分支之间进行知识交流.
- 将两个分支作为生成细分图的多种方法来处理.
主要成果:
- 与现有的E2E-WSSS方法相比,提出的方法取得了更高的性能.
- 证明了分类和细分分支行之间有效的相互促进.
- 通过来自细分分支的反,展示了本地化种子的增强质量.
结论:
- 这种新的方法有效地促进了E2E-WSSS中的分类和细分分支机构之间的协作.
- 平等待遇和互动带来了更好的细分性能.
- 这项工作为优化E2E-WSSS模型提供了新的方向.
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