相关实验视频
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没有保存的重复:为类增量语义分割的原型导出和分布再平衡
概括
阶级增量语义细分 (CISS) 方法与阶级失衡作斗争. 我们的STAR方法重复原型并使用新的损失来保持旧知识,同时学习新课程,实现最先进的结果.
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:
- 类增量语义细分 (CISS) 允许逐步学习新类,同时保留旧类的知识.
- 班级失衡是CISS的一个重大挑战,由于训练数据偏差,导致对新学习的班级的偏见.
- 当前的CISS方法往往无法充分解决失衡问题,导致性能下降.
研究的目的:
- 提出一种新的CISS方法,STAR,有效地解决阶级不平衡问题.
- 开发一个原型重播策略,重新整合过去的类信息,而不需要额外的存储.
- 引入新的损失函数,保留旧的类特征,并改善类似类之间的歧视.
主要方法:
- STAR方法利用原型重复通过重新引入以前类的缺失比例到当前的培训样本.
- 开发了一种原型偏差技术,以推断过去的类原型,整合分类器和特征提取器模式.
- 引入了两种新的损失函数,即旧类特征维护 (OCFM) 和相似感知差别 (SAD) 损失,以执行跨任务特征约束.
主要成果:
- 在Pascal VOC 2012和ADE20 K数据集上的实验证明了STAR的有效性.
- 拟议的方法在类增量语义细分方面实现了最先进的性能.
- 星号成功地减轻了类不平衡,并保留了以前学习的类的知识.
结论:
- 在CISS中,STAR提供了一个强大的解决方案来应对阶级不平衡的挑战.
- 原型重播和新浪损失功能有助于提高性能和知识保留.
- 这项研究推进了用于语义细分的增量学习领域.
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