Knowledge Commons of Institute of Automation,CAS
Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development | |
Zhang XY(张昕悦)1![]() ![]() ![]() | |
2021-05 | |
会议名称 | International Conference on Cognitive Systems and Signal Processing |
会议日期 | 2020-12 |
会议地点 | 珠海 |
摘要 | Visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning. |
收录类别 | EI |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 虚实融合与迁移学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52162 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Yang X(杨旭) |
作者单位 | 1.中国科学院自动化所 2.美团 |
推荐引用方式 GB/T 7714 | Zhang XY,Yang X,Liu ZY,et al. Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development[C],2021. |
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 | ||
FUSING~1.PDF(323KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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