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

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

401
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
401
Introduction to Learning01:18

Introduction to Learning

532
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
532
Observational Learning01:12

Observational Learning

314
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
314
Associative Learning01:27

Associative Learning

576
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...
576
Neural Circuits01:25

Neural Circuits

1.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.6K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.7K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.7K

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

[Ultrasound-synergized targeted nanoparticles suppress proliferation, migration and invasion of hypoxic lung cancer cells <i>in vitro</i>].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026
Same author

Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection.

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

AvatarVTON: 4D Virtual Try-On for Animatable Avatars.

IEEE transactions on visualization and computer graphics·2026
Same author

Development and internal validation of a clinical prediction model for septic shock in pediatric respiratory syncytial virus bronchiolitis based on routine blood biomarkers and concomitant fungal infection.

Frontiers in cellular and infection microbiology·2026
Same author

RSV-infected children with mixed infections: clinical features and early predictive indicators of codetection with <i>Streptococcus pneumoniae</i> and <i>Haemophilus influenzae</i>.

Frontiers in pediatrics·2026
Same author

Globally doubled methane emissions from nutrient-enriched rivers.

National science review·2026
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Sep 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

579

通过知识图和图形卷积网络进行传导式零射击学习.

Qiong Li1,2, Xin Sun3, Junyu Dong2

  • 1Science and Information College, Qingdao Agricultural University, Qingdao, 266109, China.

Scientific reports
|August 6, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的传导式零射击学习方法,使用知识图和图形卷积网络来改善对象识别的看不见的类别. 该方法通过利用语义关系和伪注释来提高分类准确性,优于现有的最先进技术.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

相关实验视频

Last Updated: Sep 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

579
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 零射击学习 (ZSL) 旨在通过从可见类别转移知识来识别未见类别中的对象.
  • 目前的ZSL方法由于有限的可视数据和语义关系而与域移动作斗争,阻碍了性能.
  • 在零射击学习 (ZSL) 中存在一个重要的领域转移问题,它限制了深度学习模型的性能.

研究的目的:

  • 提出一种新的传导式零射击学习 (ZSL) 方法来解决域移动问题.
  • 利用知识图 (KG) 和图形卷积网络 (GCN) 来改进ZSL分类.
  • 通过有效地从可见类别转移知识来增强未见对象类别的识别.

主要方法:

  • 构建一个知识图,每个节点代表一个由其语义嵌入编码的类别.
  • 使用浅度图形卷积网络 (GCN) 来学习被视类监督的分类器.
  • 使用双过器模块与匈牙利算法用于集群未见的样本和分配伪注释.
  • 整合一个传导设置,准确分类的隐形样本更新模型参数.

主要成果:

  • 拟议的传导式ZSL方法在基准数据集上实现了最先进的性能.
  • 在AWA2上实现了47.36%的准确性,在ImageNet50上达到30.69%的准确性,在ImageNet100上达到18.87%的准确性.
  • 与现有的零射击学习方法相比,证明了4-10%的改进.

结论:

  • 拟议的基于KG和GCN的传导式ZSL方法有效地减轻了域转移.
  • 伪注释和传导学习的整合显著提高了未见类别的分类准确性.
  • 这种方法为推进计算机视觉中零射击学习能力提供了一个有希望的方向.