Knowledge Commons of Institute of Automation,CAS
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition | |
chen yuxin1,2![]() ![]() ![]() ![]() ![]() | |
2021-10 | |
会议名称 | Proceedings of the IEEE/CVF international conference on computer vision |
会议日期 | 2021-10 |
会议地点 | 线上 |
摘要 | Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology Refinement Graph Convolution (CTR-GC) to dynamically learn different topologies and effectively aggregate joint features in different channels for skeleton-based action recognition. The proposed CTR-GC models channel-wise topologies through learning a shared topology as a generic prior for all channels and refining it with channel-specific correlations for each channel. Our refinement method introduces few extra parameters and significantly reduces the difficulty of modeling channel-wise topologies. Furthermore, via reformulating graph convolutions into a unified form, we find that CTR-GC relaxes strict constraints of graph convolutions, leading to stronger representation capability. Combining CTR-GC with temporal modeling modules, we develop a powerful graph convolutional network named CTR-GCN which notably outperforms state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
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/173211/57583 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | yuan chunfeng |
作者单位 | 1.NLPR, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology 4.School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University |
第一作者单位 | 模式识别国家重点实验室 |
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 | chen yuxin,zhang ziqi,yuan chunfeng,et al. Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition[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 | ||
Chen_Channel-Wise_To(7181KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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