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相关概念视频

Deductive Reasoning01:16

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction from inductive reasoning. It uses a general principle or law to predict specific results. From these general principles, a scientist can predict specific results that remain valid as long as the general principles are correct.For example, a researcher can make specific predictions from the hypothesis "butterflies are attracted...
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Components of Language01:24

Components of Language

Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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大型语言模型驱动的知识图推理用于增强语义细分的语义细分.

Jinhe Su1, Xiaorong Zhang1, Yang Luo1

  • 1The School of Computer Engineering, Jimei University, Xiamen, 361021, China.

Scientific reports
|December 10, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了使用大型语言模型 (LLM) 创建用于遥感场景细分的通用知识图的新框架. 这提高了各种城市,农村和港口环境的适应性和稳定性.

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科学领域:

  • 地理空间人工智能 人工智能
  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉

背景情况:

  • 城市场景细分对于3D城市建模和城市规划等遥感应用至关重要.
  • 由于数据集特定的知识图,现有的方法难以泛化.

研究的目的:

  • 开发一种新的远程传感语义细分框架,使用通用知识图.
  • 为了提高城市场景的适应性和稳定性,在各种数据源中了解城市场景.

主要方法:

  • 利用大型语言模型 (LLM) 来从多来源的地理空间数据中构建一个通用知识图.
  • 实现图形构造模块,用于提取跨域语义关系.
  • 使用知识图融合模块 (KGFusion) 将图集成到语义细分网络中.

主要成果:

  • 在UAVid数据集上实现了70.94%的mIoU,在混合数据集上达到63.23%.
  • 比基线方法的表现分别高出0.43%和1.04%.
  • 在跨领域场景中证明了效率和适应性.

结论:

  • 拟议的框架有效地增强了遥感语义细分,使用通用知识图.
  • 该方法显示出显著的稳定性和在复杂的城市环境中更广泛应用的潜力.