Knowledge Commons of Institute of Automation,CAS
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention | |
Guyue Hu1,3,4![]() ![]() ![]() | |
2019 | |
会议名称 | IEEE International Conference on Multimedia and Expo (ICME) 2019 |
会议日期 | July 8-12, 2019 |
会议地点 | Shanghai, China |
出版者 | IEEE |
摘要 | Benefiting from its succinctness and robustness, skeleton-based human action recognition has recently attracted much attention. Most existing methods utilize local networks, such as recurrent networks, convolutional neural networks, and graph convolutional networks, to extract spatio-temporal dynamics hierarchically. As a consequence, the local and non-local dependencies, which respectively contain more details and semantics, are asynchronously captured in different level of layers. Moreover, limited to the spatio-temporal domain, these methods ignored patterns in the frequency domain. To better extract information from multi-domains, we propose a residual frequency attention (rFA) to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. To optimize the whole process, we also propose a soft-margin focal loss (SMFL), which can automatically conducts adaptive data selection and encourages intrinsic margins in classifiers. Extensive experiments are performed on several large-scale action recognition datasets and our approach significantly outperforms other state-of-the-art methods. |
关键词 | Action recognition frequency attention synchronous local and non-local learning soft-margin focal loss |
DOI | 10.1109/ICME.2019.00212 |
收录类别 | EI |
七大方向——子方向分类 | 类脑模型与计算 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23235 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Shan Yu |
作者单位 | 1.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Guyue Hu,Bo Cui,Shan Yu. Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention[C]:IEEE,2019. |
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[ICME2019] Skeleton-(271KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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