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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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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...
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Detection of Black Holes01:10

Detection of Black Holes

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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OW-Adapter:人类辅助的开放世界对象检测与几个例子.

Suphanut Jamonnak, Jiajing Guo, Wenbin He

    IEEE transactions on visualization and computer graphics
    |October 23, 2023
    PubMed
    概括

    本研究介绍了OW-Adapter,一个框架,使预训练的物体检测器能够识别未知的物体. 这种方法降低了注释成本,并改善了已知和未知类的检测.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 开放世界对象检测 (OWOD) 旨在识别已知和新型对象类.
    • 现有的OWOD深度学习模型需要进行架构更改,从头开始培训和广泛的注释.

    研究的目的:

    • 开发一个框架,使预先训练的通用物体探测器能够执行开放世界的物体探测.
    • 解决现有OWOD方法的挑战,包括建筑修改,再培训和注释成本.

    主要方法:

    • 介绍了OW-Adapter,一个视觉分析框架,作为预训练检测器的适配器.
    • 开发了一种方法来识别,总结和注释未知的物体,以最小的人力努力.
    • 在预先训练的探测器中集成了一种轻量级分类器,用于新注释的未知类.

    主要成果:

    • 在常见的对象识别和自动驾驶领域证明了框架的有效性.
    • 展示了OW-Adapter可以扩展预训练的探测器来检测未知的物体.
    • 在检测已知和未知对象类的同时实现了改进.

    结论:

    • OW-Adapter提供了一个简单有效的解决方案,用于将一般物体探测器扩展到开放世界的环境中.

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  • 该框架减少了对未知的类进行广泛的再培训和昂贵的注释的需求.
  • 这种方法增强了现有的计算机视觉模型的多功能性,用于各种应用.