FCPN:修剪冗余的部分和整体关系,以实现更简单的模式解析
Zhongqi Lin1, Linye Xu1, Zengwei Zheng1
1School of Computer and Computing Science, Hangzhou City University, Zhejiang 310015, PR China.
本研究引入了一种冗余关联消除网络 (RAEN),通过消除过度泛化来改进模式解析. 实验表明,RAEN在面部和人类细分任务中显著增强了语义边界定义.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 模式识别 模式识别
背景情况:
- 模式解析方法经常通过结合不同的相关性来产生过度概括和冗余表示.
- 现有的方法在复杂的细分任务中与详细的语义边界定义作斗争.
研究的目的:
- 为了简化模式解析和提高细分精度.
- 引入一种新的网络架构,以消除在模式解析中冗余的关联.
主要方法:
- 提出了一个冗余协会消除网络 (RAEN).
- 集成的囊注意力扭曲器 (CATs) 来改进部分和整体的关系.
- 使用囊注意力路由协议 (CARA) 来防止冗余的投票信号.
主要成果:
- 雷恩有效地削减了零件和批发之间的薄弱和可互换的关系.
- 只有符合特定多样性和凝聚力标准的初级实体才能更新高级实体.
- 卡拉保护免受不必要的投票信号,提高整体表现.
结论:
- 与面部和人类细分现有方法相比,RAEN表现出优越的性能.
- 拟议的网络擅长定义详细的语义边界,在模式解析中提供了显著的进步.
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