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

Prosopagnosia01:24

Prosopagnosia

104
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
104
Vision01:24

Vision

52.2K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.2K
Visual Agnosia01:12

Visual Agnosia

165
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
165

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

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same author

Construction of phosphorus and cobalt co-modified tubular carbon nitride with dual reaction sites for boosted imidacloprid degradation.

Journal of colloid and interface science·2026
Same author

Latent profile analysis of digital health literacy among community-dwelling older adults and its influencing factors.

Digital health·2026
Same author

Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale.

Scientific reports·2026
Same author

Clip Combined With Rubber Band vs Clip-Assisted Endoscopic Retrograde Cholangiopancreatography for Difficult Biliary Cannulation in Periampullary Diverticulum: A Propensity Score-Matched Analysis.

Clinical and translational gastroenterology·2026
Same author

Novel Traction-Assisted Endoscopic Resection for Superficial Non-ampullary Duodenal Epithelial Tumors: 15-Year Experience from a Large Tertiary Center.

Digestive diseases and sciences·2026
Same journal

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Quantum Rényi α-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systems·2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

428

未知对象的无监督识别,用于开放世界的对象检测.

Ruohuan Fang, Guansong Pang, Wenjun Miao

    IEEE transactions on neural networks and learning systems
    |April 28, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的方法,通过减少标签偏差来改善开放世界物体检测 (OWOD). 这种新的方法有效地识别未知的物体,同时保持对已知的物体的准确性.

    更多相关视频

    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

    8.9K
    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    477

    相关实验视频

    Last Updated: May 9, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    428
    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

    8.9K
    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    477

    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:

    • 开放世界对象检测 (OWOD) 旨在在动态环境中检测已知和未知对象.
    • 现有的OWOD模型与标签偏差作斗争,将未知对象错误分类为背景.
    • 这限制了模型逐步学习新知识的能力.

    研究的目的:

    • 提出一个新的模块,以消除OWOD中的标签偏差.
    • 开发一种准确识别真正未知的物体的方法.
    • 为了提高OWOD模型的概括能力.

    主要方法:

    • 介绍了基于重建错误的韦布尔 (REW) 模型,用于未知对象的无监督识别.
    • 利用韦布尔模型从对象发生频率中学习.
    • 开发了REW增强的对象本地化网络 (ROLNet),通过扩展伪未知对象来完善检测.

    主要成果:

    • 拟议的方法在检测未知物体方面明显优于最先进的技术 (SOTA).
    • 在MS COCO数据集上检测已知对象类时保持竞争性表现.
    • 在LVIS和Objects365数据集上证明了改进的概括性.

    结论:

    • REW模型和ROLNet有效地解决了OWOD中的标签偏差.
    • 该方法增强了对未知的物体的检测和整体模型的概括性.
    • 这项工作在现实,动态的物体检测场景中提升了AI的能力.