Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes
文献类型：期刊论文
| 作者 | Lyu, Mengyao6; Han, Hu4,5; Bai, Xiangzhi1,2,3 |
| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
 |
| 出版日期 | 2021-08-16 |
| 页码 | 18 |
| 关键词 | Embedding-based method image classification knowledge transfer zero-shot learning (ZSL) |
| ISSN号 | 2168-2216 |
| DOI | 10.1109/TSMC.2021.3102834 |
| 英文摘要 | The goal of zero-shot learning (ZSL) is to transfer knowledge learned from seen classes during training to unseen classes for testing, with the help of auxiliary information, such as attributes and descriptions. Most of the existing methods view ZSL as a label-embedding problem, in which class and image representations are embedded to a common space. However, many methods either show a bias toward seen classes caused by the projection domain-shift problem, or sacrifice the performance of seen classes to generalize to unseen ones. In this article, we present an embedding approach for ZSL, which is motivated by human recognition memory, namely, recollection and familiarity (R&F). We propose a decoder to regularize the nonlinear mapping between the semantic space and the visual space, which represents the reasonable recollection process, and use a residual block to refine the recognition ability for seen classes, which indicates the familiarity process. R&F can generalize well to unseen classes, while retaining the discriminative ability for the seen classes. Extensive experiments are conducted on Animals with Attribute (AwA1), Animals with Attributes 2 (AwA2), Attribute Pascal&Yahoo (aPY), SUN Attribute (SUN), Caltech-UCSD-Birds 200-2011 (CUB), and ImageNet databases. As qualitative and quantitative results show, the proposed approach outperforms state-of-the-art embedding-based methods by a large margin and significantly alleviates the projection domain-shift problem. |
| 资助项目 | National Key Research and Development Program of China[2019YFB1311301] ; National Natural Science Foundation of China[U1736217] ; Youth Innovation Promotion Association CAS[2018135] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000732300600001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17944]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Bai, Xiangzhi |
| 作者单位 | 1.Beihang Univ, Adv Innovat Ctr Biomed Engn, Beijing 100083, Peoples R China 2.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China 3.Beihang Univ, Image Proc Ctr, Beijing 102206, Peoples R China 4.Peng Cheng Lab, Shenzhen 518055, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 6.Beihang Univ, Image Proc Ctr, Beijing 102206, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lyu, Mengyao,Han, Hu,Bai, Xiangzhi. Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021:18. |
| APA | Lyu, Mengyao,Han, Hu,&Bai, Xiangzhi.(2021).Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,18. |
| MLA | Lyu, Mengyao,et al."Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021):18. |
入库方式： OAI收割
来源：计算技术研究所
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