Knowledge Commons of Institute of Automation,CAS
Structural Attention Enhanced Continual Meta-Learning for Graph Edge Labeling Based Few-Shot Remote Sensing Scene Classification | |
Li FM(李非墨)1; Li SB(李帅博)2; Fan XX(樊鑫鑫)3; Li X(李雄)4; Chang HX(常红星)1 | |
发表期刊 | Remote Sensing |
2022-01 | |
期号 | 14页码:485 |
摘要 | Scene classification is one of the fundamental techniques shared by many basic remote sensing tasks with a wide range of applications. As the demands of catering with situations under high variance in the data urgent conditions are rising, a research topic called few-shot scene classification is receiving more interest with a focus on building classification model from few training samples. Currently, methods using the meta-learning principle or graphical models are achieving state-of-art performances. However, there are still significant gaps in between the few-shot methods and the traditionally trained ones, as there are implicit data isolations in standard meta-learning procedure and less-flexibility in the static graph neural network modeling technique, which largely limit the data-to-knowledge transition efficiency. To address these issues, this paper proposed an novel few-shot scene classification algorithm based on a different meta-learning principle called continual meta-learning, which enhances the inter-task correlation by fusing more historical prior knowledge from a sequence of tasks within sections of meta-training or meta-testing periods. Moreover, as to increase the discriminative power between classes, a graph transformer is introduced to produce the structural attention, which can optimize the distribution of sample features in the embedded space and promotes the overall classification capability of the model. The advantages of our proposed algorithm are verified by comparing with nine state-of-art meta-learning based on few-shot scene classification on three popular datasets, where a minimum of a 9% increase in accuracy can be observed. Furthermore, the efficiency of the newly added modular modifications have also be verified by comparing to the continual meta-learning baseline. |
关键词 | remote sensing scene classification few shot learning continual meta-learning graph transformer |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
DOI | 10.3390/rs14030485 |
URL | 查看原文 |
收录类别 | SCIE |
语种 | 英语 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47435 |
专题 | 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Chang HX(常红星) |
作者单位 | 1.中国科学院自动化研究所 2.中央财经大学 3.北京航空航天大学 4.中国矿业大学 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li FM,Li SB,Fan XX,et al. Structural Attention Enhanced Continual Meta-Learning for Graph Edge Labeling Based Few-Shot Remote Sensing Scene Classification[J]. Remote Sensing,2022(14):485. |
APA | Li FM,Li SB,Fan XX,Li X,&Chang HX.(2022).Structural Attention Enhanced Continual Meta-Learning for Graph Edge Labeling Based Few-Shot Remote Sensing Scene Classification.Remote Sensing(14),485. |
MLA | Li FM,et al."Structural Attention Enhanced Continual Meta-Learning for Graph Edge Labeling Based Few-Shot Remote Sensing Scene Classification".Remote Sensing .14(2022):485. |
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 | ||
期刊1 Structural Atten(2512KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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。
修改评论