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Distributed Zero-Shot Learning for Visual Recognition

delete2026-03-18
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PRE
AI
Z
Zhi Chen
Y
Yadan Luo
Z
Zi Huang
J
Jingjing Li
S
S. Wang
喻
喻歆 (Xin Yu)
DOI：10.1109/tmm.2026.3673561delete
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摘要

摘要

En 中文
本文提出了一种分布式零样本学习（DistZSL）框架，能够充分利用去中心化数据为未见类别学习有效模型。考虑到分布式节点间的数据异构性问题，我们引入两个关键组件以确保DistZSL的有效学习：跨节点属性正则化和全局属性-视觉共识。我们提出的跨节点属性正则化强制不同节点间属性特征的距离相似，以此方式使整体属性特征空间在学习过程中保持稳定，从而促进视觉-属性（V2A）关系的建立。随后，我们引入全局属性-视觉共识以缓解由单个节点学习的有偏V2A映射。具体而言，我们强制属性与视觉特征分布间的双向映射在不同节点上保持一致。因此，学习到的稳定V2A映射能显著提升不同节点间的零样本学习性能。大量实验表明，DistZSL在分布式数据学习方面优于当前最优方法。
Keyword:
Distributed learning
federated learning
zero-shot learning

期刊

IEEE Transactions on Multimedia 封面图
IEEE Transactions on Multimedia
IF:
9.7
论文数:
4.6K
被引数:
2.4W

机构

U
university of electronic science and technology of china
学者数:
1.3W
论文数: 4.8K
被引数: 4
T
the university of queensland
学者数:
3.1K
论文数: 1.2K
被引数: 0
U
university of southern queensland
学者数:
1.1K
论文数: 629
被引数: 2
学者 查看更多机构
引用论文

引用论文

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