关于开放词汇检测和细分的调查:过去,现在和未来
概括
本调查审查了开放词汇检测 (OVD) 和细分 (OVS),使模型能够识别超出预定义类别的对象. 它基于弱监督信号对方法进行了分类,为现场理解的进步提供了见解.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对象检测和细分是场景理解的基础,但受到昂贵的手动标签的限制.
- 现有的模型由于小,预定义的注释类别而难以泛化,造成"封闭词汇"问题.
- 开放词汇检测 (OVD) 和开放词汇细分 (OVS) 旨在通过允许超越固定的类别进行分类来克服这些限制.
研究的目的:
- 为最近在开放词汇检测和细分方面的进展提供全面的回顾.
- 为组织各种OVD和OVS任务和方法制定一个分类学.
- 分析各种方法的设计原则,挑战以及优缺点.
主要方法:
- 开发了一种分类法来对基于使用弱监督信号的方法进行分类.
- 审查的关键方法包括视觉语义空间绘图,新的视觉特征合成,区域意识培训,伪标签,知识蒸和转移学习.
- 该分类是设计为通用的,适用于对象检测,语义/实例/全视觉细分以及3D/视频理解.
主要成果:
- 该研究对OVD和OVS方法进行了分类,强调了弱监督信号的作用.
- 它分析了已开发的分类学中不同方法的优缺点.
- 该审查涵盖了广泛的任务,包括检测,各种细分类型和时空理解.
结论:
- 开放的词汇方法对于推动场景理解超越预定义类别的局限性至关重要.
- 开发的分类学为理解和比较OVD和OVS方法提供了一个结构化的框架.
- 进一步的研究可以利用这种分析来开发更强大和更可泛化的计算机视觉模型.
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