Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

426
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
426
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

481
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
481
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Compositionally-informed machine learning for solid-state electrolyte design: a structure-free approach.

Nanoscale·2026
Same author

Efficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation.

Journal of imaging·2025
Same author

Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks and Its Multi-Modal Images Extension.

IEEE journal of biomedical and health informatics·2024
Same author

Photothermal controlled-release microcapsule pesticide delivery systems constructed with sodium lignosulfonate and transition metal ions: construction, efficacy and on-demand pesticide delivery.

Pest management science·2024
Same author

Capsule Networks With Residual Pose Routing.

IEEE transactions on neural networks and learning systems·2024

相关实验视频

Updated: Jan 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

动态路由向少数射击点云语义细分的动态路由.

Guangqi Jiang1, Zhengyao Li1, Gengshen Wu2

  • 1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213159, China.

Neural networks : the official journal of the International Neural Network Society
|October 28, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个自适应相关的原型导向动态路由 (APR) 框架,通过考虑查询支持原型上下文,提高准确性和稳定性来改进少数拍摄的3D点云语义细分.

关键词:
动态路由是指动态路由.只有几次射击.一个点云点云.语义细分 语义细分是指语义细分.

相关实验视频

Last Updated: Jan 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

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:

  • 对于3D点云语义细分来说,少量射击学习至关重要,以减少数据依赖.
  • 现有的基于原型的方法往往忽略了查询和支持集之间的全球依赖性和原型级上下文,限制了准确性.

研究的目的:

  • 提出一个新的自适应相关的原型导向动态路由 (APR) 框架.
  • 通过解决当前方法的局限性,提高少数拍摄的3D点云语义细分的准确性和稳定性.

主要方法:

  • 该APR框架使用动态路由来探索查询支持原型的上下文.
  • 动态路由系数用于分析支持和查询原型之间的相关性.
  • 这些相关性被整合到损失函数中,以提高标签预测的准确性.

主要成果:

  • 拟议的APR框架显示了对基准数据集的卓越性能.
  • 在S3DIS和ScanNet数据集上的实验验证实了该方法的有效性.

结论:

  • 该APR框架有效地解决了现有方法的局限性,通过结合原型级背景.
  • 该方法显著提高了少数拍摄的3D点云语义细分的准确性和稳定性.