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

Light Acquisition02:16

Light Acquisition

9.9K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
9.9K

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

Unified Temporal-Spectral-Spatial Modeling for Robust and Generalizable Motor Imagery Brain-Computer Interfaces.

Bioengineering (Basel, Switzerland)·2026
Same author

A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population.

Bioengineering (Basel, Switzerland)·2025
Same author

From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.

Bioengineering (Basel, Switzerland)·2025
Same author

From Pixels to Precision-A Dual-Stream Deep Network for Pathological Nuclei Segmentation.

Bioengineering (Basel, Switzerland)·2025
Same author

Smart City Infrastructure Monitoring with a Hybrid Vision Transformer for Micro-Crack Detection.

Sensors (Basel, Switzerland)·2025
Same author

Lightweight early detection of knee osteoarthritis in athletes.

Scientific reports·2025

相关实验视频

Updated: Apr 7, 2026

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

16.6K

GazeCapsNet:一个轻量级的凝视估计框架

Shakhnoza Muksimova1, Yakhyokhuja Valikhujaev2, Sabina Umirzakova1

  • 1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
概括

移动-GazeCapsNet通过将囊网络与轻量级架构集成,为移动设备提供高效和准确的目光估计. 这种新的框架通过实时处理能力实现了最先进的性能.

关键词:
囊网络是一种囊网络.眼睛的外表 眼睛的外表凝视的估计估计.轻量级架构中的轻量级架构.自我注意路由机制的路由机制.

更多相关视频

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.5K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

273

相关实验视频

Last Updated: Apr 7, 2026

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

16.6K
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.5K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

273

科学领域:

  • 计算机视觉 计算机视觉
  • 人与计算机的交互
  • 机器学习 机器学习

背景情况:

  • 视线估计对于VR,AR和驾驶员监控至关重要,但由于计算需求,当前的模型在移动部署方面遇到了困难.
  • 现有的方法往往需要复杂的预处理或大量的资源,限制了它们在移动设备上的使用.

研究的目的:

  • 为移动应用程序引入Mobile-GazeCapsNet,一个高效和准确的目光估计框架.
  • 通过利用囊网络和轻量级架构来克服现有模型的局限性.

主要方法:

  • 通过将囊网络与MobileNet v2,MobileOne和ResNet-18.8集成,开发了移动-GazeCapsNet.
  • 实现了自我注意路由 (SAR) 来取代代路由,动态分配计算资源以提高效率.
  • 消除了对面部地标检测的需求.

主要成果:

  • 在ETH-XGaze和Gaze360数据集上实现了最先进的 (SOTA) 性能,平均角误差 (MAE) 减少了高达15%.
  • 实时处理在每20毫秒的时间.
  • 仅需要1170万个参数,使其适用于资源有限的环境.

结论:

  • 移动-GazeCapsNet提供了一个实用和有效的解决方案,用于实时移动凝视估计.
  • 该框架为移动视线估计技术设定了一个新的标准,平衡准确性和效率.