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

GeoStyler: A Generalizable Geometry-Aware Diffusion-Based Approach for Direct 3D Gaussian Style Transfer.

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

Slide Deformable Transformer for High-Precision LiDAR Point Cloud Compression.

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

Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder.

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

Not all regions are equal: Spatially adaptive representation learning for efficient visual object tracking.

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

Triple Spectral Fusion for Sensor-based Human Activity Recognition.

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

Simulating the Real World: A Unified Survey of Multimodal Generative Models.

IEEE transactions on pattern analysis and machine intelligence·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: Jul 4, 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

542

适应性稀疏内存网络,以实现高效和强大的视频对象分割.

Jisheng Dang, Huicheng Zheng, Xiaohao Xu

    IEEE transactions on neural networks and learning systems
    |February 5, 2024
    PubMed
    概括
    此摘要是机器生成的。

    适应性稀疏内存网络 (ASM) 通过有效地选择关键和检索相关信息来改善视频对象分割 (VOS). 这种方法提高了准确性和速度,即使对于稀疏的视频.

    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

    405
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    相关实验视频

    Last Updated: Jul 4, 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

    542
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    405
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 基于内存的网络显示出对视频对象分割 (VOS) 的承诺.
    • 由于不灵活的内存和检索问题,现有的方法面临细分精度和效率的挑战.
    • 困难包括处理相似的外观和减少准确性与更多的框架.

    研究的目的:

    • 为高效和有效的VOS提出一个自适应的稀疏内存网络 (ASM).
    • 为了解决内存构建和阅读的局限性,以提高性能.
    • 为了提高VOS任务中的细分精度和处理速度.

    主要方法:

    • 开发了一种适应性稀疏内存构造器 (ASMC),用于根据时间变化选择性框架记忆.
    • 引入了注意力局部内存读取器 (ALMR) 以使用内存子集高效地检索信息.
    • 提出了一个注意力局部特征聚合 (ALFA) 模块,以保存关键特征并扩大受体场.

    主要成果:

    • 该ASM模型在6个VOS基准测试中实现了实时速度的最先进性能.
    • 当ASM作为插件集成到现有的基于内存的方法中时,表现出显著的性能改进.
    • 在细分低率稀疏视频方面表现出稳健性.

    结论:

    • 拟议的ASM网络为VOS提供了有效的解决方案,平衡准确性和效率.
    • ASM的模块化设计允许广泛应用和增强现有的VOS技术.
    • 该方法对具有挑战性的场景,如低率视频,特别有希望.