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

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

546
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
546
Association Areas of the Cortex01:21

Association Areas of the Cortex

8.7K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
8.7K
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

4.6K
The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
4.6K
Modeling and Similitude01:12

Modeling and Similitude

588
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
588
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

651
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
651

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

GRLT: Learning more from teachers by rethinking knowledge distillation from GNNs to MLPs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Method for Data Augmentation in Vertical Federated Learning Addressing Data Heterogeneity.

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

Hierarchical Causal Learning for Face Age Synthesis.

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

GBFRS: Robust Fuzzy Rough Sets via Granular Ball Computing.

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

Decoding epigenetic aging using plants: lessons from Arabidopsis thaliana as a short-lived model.

Science bulletin·2026
Same author

Aging drives a program of DNA methylation decay in plant organs.

Science (New York, N.Y.)·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

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

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
08:32

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

Published on: October 20, 2023

3.8K

FDSRM:一个特征驱动的风格不可知的基础模型,用于草图不多的面部图像检索.

Yingge Liu, Dawei Dai, Shuyin Xia

    IEEE transactions on neural networks and learning systems
    |November 25, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种基于特征的基础模型,用于无草图的面部图像检索 (FDSRM). 这种新的方法通过解决草图风格多样性和冲击随机性来提高准确性和概括性.

    更多相关视频

    Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
    09:49

    Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

    Published on: December 24, 2015

    14.5K
    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
    07:34

    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

    Published on: June 3, 2013

    17.8K

    相关实验视频

    Last Updated: Jan 10, 2026

    Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
    08:32

    Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

    Published on: October 20, 2023

    3.8K
    Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
    09:49

    Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

    Published on: December 24, 2015

    14.5K
    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
    07:34

    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

    Published on: June 3, 2013

    17.8K

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 人与计算机的交互

    背景情况:

    • 传统的没有素描的面部图像检索 (SLFIR) 难以处理各种素描风格和位.
    • 现有的方法通常需要高质量的草图输入,限制了实际应用.

    研究的目的:

    • 开发一种新的特征驱动的基础模型,用于无草图的面部图像检索 (FDSRM).
    • 为了创建一个强大的模型,在草图风格和冲动随机性的变化.
    • 提高SLFIR系统的准确性和概括能力.

    主要方法:

    • 提出了一个特征驱动的基础模型 (FDSRM),其中包括特征观察模块 (FOM) 和自适应融合适配器 (AFA).
    • FOM利用多位专家从草图中提取风格不变的特征.
    • 根据草图图形的进展,AFA根据草图图形的进展动态调整特征融合,并结合草图的先验.
    • 采用了面部图像-文本对齐预训练 (FAIP) 模型,以提高稳定性.

    主要成果:

    • 在早期检索准确度方面,FDSRM表现出显著的优势.
    • 该模型在多种风格的场景中展示了卓越的系统概括能力.
    • 在没有辅助信息的情况下,在定性和定量评估中取得了最先进的表现.

    结论:

    • 拟议的FDSRM有效地解决了SLFIR中素描风格多样性和冲击随机性的挑战.
    • 基于特征的方法提高了检索准确性和稳定性,优于现有方法.
    • 这项工作推进了没有草图的面部图像检索领域,提供了更实用和多功能解决方案.