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

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation.

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

XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition.

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

Human-Structure-Aware Token Position Embedding for Tokenized Pose Estimation.

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

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

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

Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization.

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

Event-Aware Instructed Assistant for Referring Video Segmentation.

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

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 1, 2025

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

2.7K

走向强大的引用图像分割.

Jianzong Wu, Xiangtai Li, Xia Li

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 5, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了强大的引用图像分割 (R-RIS) 以处理视觉语言任务中的不正确文本描述. 新的RefSegformer模型在标准和强大的引用图像细分方面取得了最先进的结果.

    更多相关视频

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
    06:48

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

    Published on: January 7, 2019

    8.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    399

    相关实验视频

    Last Updated: Jul 1, 2025

    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

    2.7K
    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
    06:48

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

    Published on: January 7, 2019

    8.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    399

    科学领域:

    • 计算机视觉 计算机视觉
    • 自然语言处理自然语言处理.
    • 人工智能的人工智能

    背景情况:

    • 引用图像细分 (RIS) 是从文本生成对象面具的关键视觉语言任务.
    • 现有的RIS模型与不准确或误导性的文本描述,称为负面句子而斗争.
    • 需要一个强大的方法来处理准确和不准确的文本指导.

    研究的目的:

    • 引入强大的引用图像分割 (R-RIS),能够处理负面句子.
    • 为R-RIS开发新的数据集和评估指标.
    • 提出一种基于变压器的新型模型,RefSegformer,用于增强RIS.

    主要方法:

    • 通过增加现有的RIS数据集以负句子创建了三个R-RIS数据集.
    • 开发了统一的指标来评估积极和消极文本输入的表现.
    • 拟议的RefSegformer,是一种具有基于令牌的融合模块的变压器模型,可适应R-RIS.

    主要成果:

    • 在已建立的RIS基准上,RefSegformer实现了最先进的性能.
    • 该模型在新的R-RIS环境中展示了强大的功能,有效地处理负面句子.
    • 建立了强大的引用图像细分的新基准.

    结论:

    • 拟议的R-RIS表述和RefSegformer模型在视觉语言任务中提供了显著的进步.
    • 该方法有效地解决了图像细分中的不准确文本描述的挑战.
    • 这项工作为未来关于引用图像细分的研究提供了坚实的基础.