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

Classification of Systems-I01:26

Classification of Systems-I

219
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
219
Classification of Systems-II01:31

Classification of Systems-II

179
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
179
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

34.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
34.1K
Labeling Emotion01:20

Labeling Emotion

184
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
184
Classification of Signals01:30

Classification of Signals

543
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
543
Aggregates Classification01:29

Aggregates Classification

348
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
348

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

Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.

Bioengineering (Basel, Switzerland)·2026
Same author

Personalized federated learning for medical vision-language models via efficient fine-tuning and uncertainty-aware disentanglement.

Journal of biomedical informatics·2026
Same author

Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification.

Pathology international·2026
Same author

Dual-model weight selection and self-knowledge distillation for medical image classification.

Computers in biology and medicine·2026
Same author

Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness.

Bioengineering (Basel, Switzerland)·2026
Same author

Deep-learning-based automatic liver segmentation using computed tomography images in dogs.

Frontiers in veterinary science·2025

相关实验视频

Updated: Jul 24, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

在动漫插图中的多标签分类基于层次的属性关系.

Ziwen Lan1, Keisuke Maeda2, Takahiro Ogawa2

  • 1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究介绍了一种层次图卷积网络 (GCN) 模型,用于在动漫插图中分类多个属性. 该模型有效地捕捉了属性关系和创建者预期的细节,以提高准确性.

关键词:
动漫插图 动漫插图属性分类 属性分类 属性分类生成性的对抗性网络.图表 卷积网络 卷积网络一个层次的分类分类.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
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

11.9K

相关实验视频

Last Updated: Jul 24, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
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

11.9K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 动漫插图属性分类是复杂的,因为微妙的,创作者打算的特征.
  • 现有的方法很难捕捉属性之间的等级和共发生关系.

研究的目的:

  • 为动漫插图提出一种新的多模式多标签属性分类模型.
  • 通过利用等级结构和图形卷积网络来提高属性分类的准确性.

主要方法:

  • 开发了一个图形卷积网络 (GCN) 模型,包含层次聚类和标签分配.
  • 组织属性信息成一个层次特征来表示下属关系.
  • 建立基于频率和衍生规则的层次属性结构.

主要成果:

  • 提出的基于GCN的模型在多标签属性分类中实现了高精度.
  • 证明有效捕获属性共发生和附属关系.
  • 实验结果显示在多个数据集上优于现有方法.

结论:

  • 层次GCN模型对于动漫插图属性分类是有效和可扩展的.
  • 整合层次结构显著提高了模型理解复杂属性关系的能力.
  • 该方法为视觉媒体的详细内容分析提供了一个有前途的方法.