Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding
Dan Li; Changde Du; Haibao Wang; Qiongyi Zhou; Huiguang He
发表期刊IEEE Transactions on Multimedia
2022-07
卷号24页码:3287-3299
摘要

Multi-label semantic decoding is a challenging task with great scientific significance and application value. The existing methods mainly focus on label learning and ignore the amount of information contained in the sample itself,especially non-image sample,which may limit their performance. To address these issues,we propose a novel semi-supervised modality assistance co-training network,which utilizes image modality to assist non-image modality for multi-label learning. In real application,there are two thorny issues: (i) non-image modality tends to be missing owing to the difficulty in obtaining them; (ii) although the image modality is easy to obtain from the Internet,image label annotation is still time-consuming and expensive. Therefore,the proposed method utilizes a small number of paired & labeled images and non-image modalities,and a large number of unpaired & unlabeled images from web sources to improve results. It consists of the modality-specific feature generators,the feature translators and the label relationship network. Specifically,the modality-specific feature generators are used to generate different features (views) for each modality. Semantic translators are employed to capture the relationship between the paired modalities and impute the missing modality feature by using unpaired & unlabeled images. Label relation network is a graph convolution network (GCN) aiming to capture the correlation between labels. To mine the information in unlabeled features,the co-training mechanism is considered. With this mechanism,we introduce a multi-view orthogonality constraint and a multi-label co-regularization constraint. Extensive experiments on three computer vision and neuroscience datasets demonstrate the effectiveness of the proposed method.

DOI10.1109/TMM.2021.3104980
收录类别SCI
七大方向——子方向分类脑机接口
国重实验室规划方向分类人工智能基础前沿理论
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50896
专题脑图谱与类脑智能实验室_神经计算与脑机交互
通讯作者Huiguang He
推荐引用方式
GB/T 7714
Dan Li,Changde Du,Haibao Wang,et al. Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding[J]. IEEE Transactions on Multimedia,2022,24:3287-3299.
APA Dan Li,Changde Du,Haibao Wang,Qiongyi Zhou,&Huiguang He.(2022).Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding.IEEE Transactions on Multimedia,24,3287-3299.
MLA Dan Li,et al."Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding".IEEE Transactions on Multimedia 24(2022):3287-3299.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
J70-Deep Modality As(2627KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Dan Li]的文章
[Changde Du]的文章
[Haibao Wang]的文章
百度学术
百度学术中相似的文章
[Dan Li]的文章
[Changde Du]的文章
[Haibao Wang]的文章
必应学术
必应学术中相似的文章
[Dan Li]的文章
[Changde Du]的文章
[Haibao Wang]的文章
相关权益政策
暂无数据
收藏/分享
文件名: J70-Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。