期刊文献+

基于语义对齐和重构的零样本学习算法 认领

Zero-shot learning based on semantic alignment and reconstruction
在线阅读 下载PDF
收藏 分享 导出
摘要 为同时缓解零样本学习算法中固有的枢纽问题和域漂移问题,提出一种基于语义对齐和重构的零样本学习算法。以语义特征嵌入到图像空间的神经网络映射模型为基础,对模型添加语义原型和图像原型对齐的约束条件进一步缓解高维向量枢纽问题对标签预测的影响;对模型添加语义特征重构建的约束条件,缓解域漂移问题对识别正确率的影响。实验结果表明,所提算法在AwA和CUB数据集上达到了较优的识别正确率,验证了其有效性。 To alleviate the hubness problem and domain drift problem in the zero-shot learning,a reverse mapping method via semantic alignment and semantic reconstruction was proposed.Based on the neural network mapping model with semantic features embedded in image space,the constraints on semantic prototype and image prototype alignment were added to the model to improve the impact of high-dimensional vector hubness problems on label prediction.The constraint on semantic feature reconstruction was added to the model to improve the impact of domain drift problem on recognition accuracy.Experimental results show that the proposed algorithm achieves good recognition accuracies on AwA and CUB datasets,which verifies its effectiveness.
作者 王紫沁 杨维 WANG Zi-qin;YANG Wei(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)
出处 《计算机工程与设计》 北大核心 2021年第1期70-75,共6页 Computer Engineering and Design
关键词 零样本学习 语义对齐 枢纽问题 语义重构 域漂移 zero-shot learning semantic alignment hubness problem semantic reconstruction domain drift
  • 相关文献
202103读书月活动
维普数据出版直通车
今日学术
投稿分析
职称考试

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部 意见反馈