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

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

Wireless Sensing and Networking for the Internet of Things.

Sensors (Basel, Switzerland)·2023
Same author

A Novel Floating High-Voltage Level Shifter with Pre-Storage Technique.

Sensors (Basel, Switzerland)·2022
Same author

Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks.

PloS one·2021
Same author

Outcomes from international field trials with Male Aedes Sound Traps: Frequency-dependent effectiveness in capturing target species in relation to bycatch abundance.

PLoS neglected tropical diseases·2021
Same author

Optimizing spatial healthcare assets with Internet of Things.

Health information science and systems·2018
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章

相关实验视频

Updated: Jan 7, 2026

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

990

基于多层属性指导的自适应多扩展卷积网络用于图像美学评估.

Sumei Li1, Mingxuan Xie1, Wei Xiang2,3

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Journal of imaging
|December 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的多层次属性导向自适应多扩展卷积网络 (MAADN),用于图像审美评估. MAADN通过考虑层次属性相互作用和减轻预处理扭曲来提高准确性.

关键词:
审美属性 审美属性 审美属性注意力机制注意力机制扩张的卷积扩张的卷积.图像美学评估的评估

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

2.2K

相关实验视频

Last Updated: Jan 7, 2026

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

990
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

2.2K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 图像处理 图像处理

背景情况:

  • 图像美学评估 (IAA) 对研究和应用至关重要.
  • 现有的IAA方法忽视了层次的属性-特征交互,并在预处理过程中遭受比例扭曲,失去美学信息.

研究的目的:

  • 提出一个新的网络,MAADN,解决IAA的局限性.
  • 为了利用多层次的属性特征来指导审美评估.
  • 通过自适应扩展卷积来减少图像预处理的负面影响.

主要方法:

  • 开发了一个双分支架构:一个用于多层次的属性特征提取,另一个用于属性引导的美学特征学习.
  • 引入了使用视觉注意力的基于注意力的属性引导美学模块 (AGAM).
  • 设计了一个自适应多扩展速率卷积模块 (AMDM),用于扩展卷积特征的自适应融合.

主要成果:

  • 拟议的MAADN模型显著优于当前最先进的IAA方法.
  • 实验结果验证了双分支架构和自适应模块的有效性.
  • 视觉分析证实MAADN精确地定位了美学关键区域.

结论:

  • MAADN有效地解决了IAA中的等级交互和预处理扭曲.
  • 拟议的模块的适应性增强了灵活性和性能.
  • MAADN为图像美学评估提供了一个强大而准确的解决方案.