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

Concepts and Prototypes01:24

Concepts and Prototypes

82
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
82
Associative Learning01:27

Associative Learning

276
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
276
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

399
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
399
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

104
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...
104
Visual System01:26

Visual System

475
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
475
Classification of Systems-I01:26

Classification of Systems-I

167
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:
167

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

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EVDI++: Event-based Video Deblurring and Interpolation via Self-Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Interacted Planes Reveal 3D Line Mapping.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Rejoining fragmented ancient bamboo slips with physics-driven deep learning.

Nature communications·2026
Same author

Understanding Data Influence With Differential Approximation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Revisiting Fine-Grained Image Analysis by Semantic-Part Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

CLASH-CTTA: Class-Wise Shift-Aware Hierarchical Continual Test-Time Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DA-Cal: Towards Cross-Domain Calibration in Semantic Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Multi-Dimensional Quality Assessment for Single-Image-to-3D Contents: Dataset and Model.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章
  1. 首页
  2. 普遍细粒度的视觉分类通过概念指导学习学习.
  1. 首页
  2. 普遍细粒度的视觉分类通过概念指导学习学习.

相关实验视频

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.5K

普遍细粒度的视觉分类通过概念指导学习学习.

Qi Bi, Beichen Zhou, Wei Ji

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 3, 2025

    在PubMed 上查看摘要

    概括
    此摘要是机器生成的。

    本研究介绍了概念指导学习 (CGL),这是一个在具有挑战性的现实场景中用于细粒度视觉分类 (FGVC) 的通用框架. CGL通过建模类别概念来增强表示学习,在各种数据集上获得最先进的结果.

    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

    11.8K
    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
    05:35

    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

    Published on: April 19, 2017

    6.6K

    相关实验视频

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    6.5K
    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

    11.8K
    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
    05:35

    Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

    Published on: April 19, 2017

    6.6K

    科学领域:

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

    背景情况:

    • 现有的细粒度视觉分类 (FGVC) 方法因依赖对象中心假设而与场景中心和不利视角图像作斗争.
    • 在具有挑战性的场景中错误/过度的功能激活会降低细粒度表示学习.
    • 对于超越理想的以对象为中心的视图的现实应用,需要一个通用的FGVC框架.

    研究的目的:

    • 开发一个通用的细粒度视觉分类 (FGVC) 框架,适应现实世界的场景.
    • 解决现有FGVC方法在场景中心和不利视角条件中的局限性.
    • 为强大的视觉分类提出一种新的概念导向学习方法.

    主要方法:

    • 提出的概念引导学习 (CGL) 框架,通过遗传和歧视性概念来建模类别.
    • 歧视性概念通过概念挖掘,融合和约束来引导细粒度的表示学习.
    • 引入了59,994个样本的细粒度土地覆盖分类数据集 (FGLCD),以弥补数据集的差距.

    主要成果:

    • 在传统的FGVC任务上,CGL表现出了竞争力.
    • 在细粒度的空中场景和场景中心的街头场景上取得了最先进的结果.
    • 在物体重新识别和细粒度空中物体检测方面展示了强大的概括能力.

    结论:

    • 概念指导学习 (CGL) 提供了一个强大的和通用的解决方案,用于细粒度的视觉分类在各种现实世界的条件下.
    • 拟议的CGL框架有效地克服了以场景为中心和不利视角图像所带来的挑战.
    • 新的FGLCD数据集有助于进一步研究具有挑战性的FGVC场景.