普遍细粒度的视觉分类通过概念指导学习学习
概括
本研究介绍了概念指导学习 (CGL),这是一个在具有挑战性的现实场景中用于细粒度视觉分类 (FGVC) 的通用框架. CGL通过建模类别概念来增强表示学习,在各种数据集上获得最先进的结果.
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:
- 现有的细粒度视觉分类 (FGVC) 方法因依赖对象中心假设而与场景中心和不利视角图像作斗争.
- 在具有挑战性的场景中错误/过度的功能激活会降低细粒度表示学习.
- 对于超越理想的以对象为中心的视图的现实应用,需要一个通用的FGVC框架.
研究的目的:
- 开发一个通用的细粒度视觉分类 (FGVC) 框架,适应现实世界的场景.
- 解决现有FGVC方法在场景中心和不利视角条件中的局限性.
- 为强大的视觉分类提出一种新的概念导向学习方法.
主要方法:
- 提出的概念引导学习 (CGL) 框架,通过遗传和歧视性概念来建模类别.
- 歧视性概念通过概念挖掘,融合和约束来引导细粒度的表示学习.
- 引入了59,994个样本的细粒度土地覆盖分类数据集 (FGLCD),以弥补数据集的差距.
主要成果:
- 在传统的FGVC任务上,CGL表现出了竞争力.
- 在细粒度的空中场景和场景中心的街头场景上取得了最先进的结果.
- 在物体重新识别和细粒度空中物体检测方面展示了强大的概括能力.
结论:
- 概念指导学习 (CGL) 提供了一个强大的和通用的解决方案,用于细粒度的视觉分类在各种现实世界的条件下.
- 拟议的CGL框架有效地克服了以场景为中心和不利视角图像所带来的挑战.
- 新的FGLCD数据集有助于进一步研究具有挑战性的FGVC场景.
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