论文标题
半循环语言 - 在计算机视觉中统一机器学习和象征推理的正式基础
Semi-Lexical Languages -- A Formal Basis for Unifying Machine Learning and Symbolic Reasoning in Computer Vision
论文作者
论文摘要
人类的愿景能够通过基于对世界的先验知识来理解现实世界中感觉输入的缺陷。机器学习由于其固有的处理不精确的能力而对计算机视觉产生了重大影响，但是基于域知识的缺乏推理框架限制了其解释复杂场景的能力。我们建议半循环语言作为处理现实世界提供的不完善令牌的正式依据。机器学习的力量用于将不完美的令牌映射到语言的字母中，符号推理用于确定语言中输入的成员。半静态语言还具有结合因素，可以防止在输入的不同部分解释半腿部令牌的变化，从而倾向于演绎以增强对单个令牌的识别质量。我们提出了案例研究，这些案例研究表明，使用这种框架比纯机器学习和纯符号方法的优势。
Human vision is able to compensate imperfections in sensory inputs from the real world by reasoning based on prior knowledge about the world. Machine learning has had a significant impact on computer vision due to its inherent ability in handling imprecision, but the absence of a reasoning framework based on domain knowledge limits its ability to interpret complex scenarios. We propose semi-lexical languages as a formal basis for dealing with imperfect tokens provided by the real world. The power of machine learning is used to map the imperfect tokens into the alphabet of the language and symbolic reasoning is used to determine the membership of input in the language. Semi-lexical languages also have bindings that prevent the variations in which a semi-lexical token is interpreted in different parts of the input, thereby leaning on deduction to enhance the quality of recognition of individual tokens. We present case studies that demonstrate the advantage of using such a framework over pure machine learning and pure symbolic methods.
