ShanghaiTech University Knowledge Management System
A Dual Attention Network with Semantic Embedding for Few-Shot Learning | |
Shipeng Yan; Songyang Zhang; Xuming He | |
2019 | |
会议录名称 | THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE |
页码 | 9079-9086 |
发表状态 | 已发表 |
摘要 | Despite recent success of deep neural networks, it remains challenging to efficiently learn new visual concepts from limited training data. To address this problem, a prevailing strategy is to build a meta-learner that learns prior knowledge on learning from a small set of annotated data. However, most of existing meta-learning approaches rely on a global representation of images and a meta-learner with complex model structures, which are sensitive to background clutter and difficult to interpret. We propose a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training data for label prediction. We implement our method via a dual-attention network and design a semantic-aware meta-learning loss to train the meta-learner network in an end-to-end manner. We validate our model on three few-shot image classification datasets with extensive ablative study, and our approach shows competitive performances over these datasets with fewer parameters. For facilitating the future research, code and data split are available: https://github.com/tonysy/STANet-PyTorch |
会议录编者/会议主办者 | Association for the Advancement of Artificial Intelligence |
关键词 | Classification (of information) Model structures Semantic Web Semantics Attention mechanisms Background clutter Classification datasets Competitive performance Global representation Limited training data Meta-learning approach Shot classification |
会议名称 | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
出版地 | 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA |
会议地点 | Honolulu, HI, United states |
会议日期 | January 27, 2019 - February 1, 2019 |
收录类别 | CPCI ; CPCI-S ; EI |
语种 | 英语 |
资助项目 | NSFC[61703195] |
WOS记录号 | WOS:000486572503077 |
出版者 | ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE |
EI入藏号 | 20203509102371 |
EI主题词 | Deep neural networks |
EI分类号 | 461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 723 Computer Software, Data Handling and Applications ; 903 Information Science ; 903.1 Information Sources and Analysis |
原始文献类型 | Proceedings Paper |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29127 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_何旭明组 信息科学与技术学院_博士生 |
通讯作者 | Xuming He |
作者单位 | ShanghaiTech University |
第一作者单位 | 上海科技大学 |
通讯作者单位 | 上海科技大学 |
第一作者的第一单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Shipeng Yan,Songyang Zhang,Xuming He. A Dual Attention Network with Semantic Embedding for Few-Shot Learning[C]//Association for the Advancement of Artificial Intelligence. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2019:9079-9086. |
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