Knowledge Commons of Institute of Automation,CAS
Domain-Oriented Semantic Embedding for Zero-Shot Learning | |
Min, Shaobo1; Yao, Hantao2; Xie, Hongtao1; Zha, Zheng-Jun1; Zhang, Yongdong1 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
2021 | |
卷号 | 23页码:3919-3930 |
通讯作者 | Xie, Hongtao(htxie@ustc.edu.cn) ; Zhang, Yongdong(zhyd73@ustc.edu.cn) |
摘要 | Zero-Shot Learning (ZSL) targets to recognize images from new classes. Existing methods focus on learning a projection function to associate the visual features and category descriptions in the seen domain, which is directly transferred to the unseen domain. However, due to the inherent domain shift, a single shared projection cannot fully capture the domain difference and similarity, thereby making the unseen samples tend to be recognized as seen categories. In this paper, we propose a novel Domain-Oriented Semantic Embedding (DOSE) network that learns specific projections for different domains to better capture the domain characteristics for unbiased ZSL. Besides a domain-shared projection, DOSE learns two auxiliary domain-specific sub-projections to model the semantic-visual association in respective seen and unseen domains. Specifically, the domain-specific projections are learned in a cycle consistency way to capture domain characteristics, and a domain division constraint is developed to penalize the margin between two domain embeddings. Furthermore, to boost semantic-visual association, a semantic-visual dual attention module is designed to automatically remove trivial information in both visual and semantic embeddings under a co-guidance learning manner. Experiments on four public benchmarks prove that the proposed DOSE is robust to the domain shift problem in ZSL and obtains an averaged 5.6% improvement in terms of harmonic mean. |
关键词 | Semantics Visualization Image recognition Image reconstruction Training Gallium nitride Search problems Zero-shot learning multi-modality embedding recognition |
DOI | 10.1109/TMM.2020.3033124 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research, and Development Program of China[2017YFC0820600] ; National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[62022076] ; National Nature Science Foundation of China[U1936210] ; National Postdoctoral Programme for Innovative Talents[BX20180358] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2017209] |
项目资助者 | National Key Research, and Development Program of China ; National Nature Science Foundation of China ; National Postdoctoral Programme for Innovative Talents ; Youth Innovation Promotion Association Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:000709093100038 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46307 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Xie, Hongtao; Zhang, Yongdong |
作者单位 | 1.Univ Sci & Technol China, Natl Engn Lab Brain Inspired Intelligence Technol, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China |
推荐引用方式 GB/T 7714 | Min, Shaobo,Yao, Hantao,Xie, Hongtao,et al. Domain-Oriented Semantic Embedding for Zero-Shot Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:3919-3930. |
APA | Min, Shaobo,Yao, Hantao,Xie, Hongtao,Zha, Zheng-Jun,&Zhang, Yongdong.(2021).Domain-Oriented Semantic Embedding for Zero-Shot Learning.IEEE TRANSACTIONS ON MULTIMEDIA,23,3919-3930. |
MLA | Min, Shaobo,et al."Domain-Oriented Semantic Embedding for Zero-Shot Learning".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):3919-3930. |
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