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相关概念视频

Newman Projections02:06

Newman Projections

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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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原型适应和投影为少数和零拍摄的3D点云语义细分的原型适应和投影.

Shuting He, Xudong Jiang, Wei Jiang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |May 30, 2023
    PubMed
    概括

    这项研究引入了用于少数射击和零射击3D点云语义细分的新方法,克服了数据限制. 查询引导原型调整 (QGPA) 模块通过调整功能和增强原型表示来显著提高性能.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 3D数据分析 3D数据分析

    背景情况:

    • 在3D点云语义细分中,少量和零射击学习面临挑战,因为3D注释数据集有限.
    • 现有的2D方法在3D中效率较低,原因是代表性较低的特征和高的类内变化.
    • 3D数据收集和注释的成本阻碍了强大的3D深度学习模型的开发.

    研究的目的:

    • 开发有效的方法,为少数射击和零射击的3D点云语义细分.
    • 解决3D点云数据中特征表示和类内变化的局限性.
    • 在稀疏和有限的3D数据集上改进语义细分的性能.

    主要方法:

    • 提出了一个查询引导原型调整 (QGPA) 模块,以在支持和查询点云功能空间之间调整原型.
    • 引入了自重建 (SR) 模块,通过重建支持面具来增强原型表示.
    • 开发了一种语义视觉投影模型,通过将类别词作为语义信息来实现零射击细分.

    主要成果:

    • QGPA模块显著缓解了点云中的大型特征类内变化.
    • 提出的方法实现了实质性的性能增长,超过了最先进的算法.
    • 在双向一拍设置下,在S3DIS和ScanNet基准值上分别实现了7.90%和14.82%的改进.

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    结论:

    • 拟议的QGPA和SR模块有效地增强了少数拍摄的3D点云语义细分.
    • 语义视觉投影模型通过弥合语义和视觉信息,使有效的零射击细分成为可能.
    • 开发的方法在处理3D语义细分的有限数据场景方面取得了重大进展.