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ETFT:用于不平衡的语义细分的等角窄框变压器.
Seonggyun Jeong1, Yong Seok Heo1,2
1Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了等角紧框变压器 (ETFT),以解决语义细分中的类失衡问题. 新型模型动态生成分类器,改善不平衡数据集的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 语义细分面临着阶级不平衡的挑战,标签分配不均.
- 现有的方法使用神经崩和等角紧框架 (ETF) 来改善小阶级歧视,但与阶级相关性作斗争.
- 当前的方法导致固定的分类器,限制适应不同输入图像的适应性.
研究的目的:
- 提出一种基于变压器的新型模型,即平角紧框变压器 (ETFT),用于不平衡的语义细分.
- 作为输入的函数,动态生成分类器,平衡类歧视和相关性.
- 增强语义细分模型在数据集上的适应性和性能,其类分布不均.
主要方法:
- ETFT模型通过在变压器架构中的ETF结构共同处理特征和分类器.
- 输入补丁令牌和ETF初始化分类器在注意力机制期间一起处理.
- 该分类器根据输入相关性进行动态调整,并与固定ETF分类器相结合.
主要成果:
- 通过ETFT模型,可以提高阶级之间的区别,同时保持上下文相关性.
- 动态生成的分类器适应输入,提高性能.
- 实验表明,拟议的方法优于ADE20K和Cityscapes数据集上的最新方法,用于不平衡的语义细分.
结论:
- ETFT模型有效地解决了语义细分中的类不平衡的挑战.
- 动态分类器生成为不同的输入数据提供了相对于固定分类器的显著优势.
- 提出的方法在不平衡的语义细分任务中表现出卓越的性能和稳定性.
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