以语义为指导的构成性场景表示框架
Qiang Liu1, Qiulei Dong2, Yangyong Zhang1
1Key Laboratory for Robot and Intelligent Technology of Shandong Province, Shandong University of Science and Technology, Qingdao, 266590, China; College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China.
概括
本研究引入了一个语义导向的框架,用于神经场景表示,使广义化到未见的环境. 该方法有效地使用语义属性表示可见和不可见的多对象场景.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 机器学习 机器学习
背景情况:
- 神经场景表示方法至关重要,但在将其推广到新的,未见的场景时,会遇到困难.
- 现有的方法在有效地代表隐形或新型环境方面面临挑战.
研究的目的:
- 开发一种新的语义导向的构成场景表现框架.
- 为了增强神经场景表示的无形场景的概括能力.
- 提高多对象场景表示的培训效率.
主要方法:
- 开发了一个框架,包括一个基线场景表示模块,一个语义映射模块和一个构成性表示策略.
- 语义映射模块学习嵌入显式语义属性和隐式场景表示之间的相关性.
- 构成策略将单个对象表示融合为多个对象表示,以提高效率.
主要成果:
- 拟议的框架成功地代表了只使用语义属性的无形场景.
- 构成策略显著提高了多对象场景的训练效率.
- 在三个数据集上的实验证实了可见和不可见的多对象场景的高精度表示.
结论:
- 语义指导的构成框架为将神经场景表示概括到看不见的环境提供了一个强大的解决方案.
- 这种方法提高了神经场景表示在复杂的现实场景中的应用性.
- 该方法证明了多对象场景表示的提高准确性和训练效率.
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