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

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

6.6K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
6.6K

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

Triglyceride levels and all-cause mortality in patients with left main coronary artery disease undergoing percutaneous coronary intervention.

Frontiers in cardiovascular medicine·2026
Same author

Threat discrimination of real-world social interactions in schizotypal traits.

Psychonomic bulletin & review·2026
Same author

Network structure shapes consensus dynamics through individual decisions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Dual Carbon Modification and Pore Structure Regulation of CuS/FeS<sub>2</sub> Bimetallic Sulfides via Multifunctional Additives toward High-Rate and Durable Sodium Storage.

Inorganic chemistry·2025
Same author

Enhancing visuospatial mapping in relational category learning.

Journal of experimental psychology. Learning, memory, and cognition·2025
Same author

Correction: Reproductive factors and risk of cardiovascular diseases and all-cause and cardiovascular mortality in American women: NHANES 2003-2018.

BMC women's health·2025
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
查看所有相关文章

相关实验视频

Updated: Mar 15, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K

层次抽象驱动了深度学习模型中类似人类的3D形状处理.

Shuhao Fu1, Philip J Kellman1, Hongjing Lu1,2

  • 1Department of Psychology, University of California Los Angeles, Los Angeles, California, United States of America.

PLoS computational biology
|March 13, 2026
PubMed
概括
此摘要是机器生成的。

深度学习模型在3D点云中的对象识别方面表现出色,但在全球形状理解方面却很难. 基于变压器的模型通过使用等级抽象更好地模仿人类的3D形状感知.

更多相关视频

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

253

相关实验视频

Last Updated: Mar 15, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

253

科学领域:

  • 计算机视觉 计算机视觉
  • 认知科学 认知科学
  • 人工智能的人工智能

背景情况:

  • 深度学习模型和人类可以从3D点云中识别物体.
  • 当前的模型实现了类似人类的性能,但可能无法开发类似的3D形状表示.
  • 现有证据表明,模型学习了局部几何结构,但可能对全球形状的理解有限.

研究的目的:

  • 调查深度学习模型是否开发与人类视觉相比的3D形状表示.
  • 测试深度学习模型具有3D全球形状有限表示的假设.
  • 将不同深度学习架构的性能与3D形状识别任务中的人类性能进行比较.

主要方法:

  • 进行了三项人体实验,操纵了点密度,物体定向,局部几何和零件配置.
  • 用基于卷积 (DGCNN) 和基于变压器 (点变压器) 的深度学习模型比较人类性能.
  • 利用消光模拟来识别驱动模型性能的关键架构特征.

主要成果:

  • 人类的表现在点密度,方向和局部几何学的变化中保持稳定.
  • 当对象的部分被混时,人类的性能显著下降,这表明对全球配置的敏感性.
  • 与基于卷积的模型相比,基于变压器的模型在实验条件下更准确地复制了人类的表现模式.
  • 在变压器模型中逐步降低采样被确定为对等级形状抽象至关重要.

结论:

  • 深度学习模型,特别是基于变压器的架构,在开发类似人类的3D形状表示方面表现出前景.
  • 通过像下方采样这样的操作进行等级抽象是模型有效捕获全球形状信息的关键.
  • 需要进一步的研究才能充分理解和复制人工智能中人类3D形状感知的细微差别.