Knowledge Commons of Institute of Automation，CAS
| Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition | |
Jinzhao Luo1,2 ; Lu Zhou1,2; Guibo Zhu1,2,3; Guojing Ge1; Beiying Yang1,2; Jinqiao Wang1,2,3,4
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| 2023-10-13 | |
| 会议名称 | The 6th Chinese Conference on Pattern Recognition and Computer Vision |
| 会议日期 | 2023年10月13日~15日 |
| 会议地点 | 福建厦门国际会议中心 |
| 摘要 | Skeleton-based action recognition has become popular in recent years due to its efficiency and robustness. Most current methods adopt graph convolutional network (GCN) for topology modeling, but GCN-based methods are limited in long-distance correlation modeling and generalizability. In contrast, the potential of convolutional neural network (CNN) for topology modeling has not been fully explored. In this paper, we propose a novel CNN architecture, Temporal-Channel Topology Enhanced Network (TCTE-Net), to learn spatial and temporal topologies for skeleton-based action recognition. The TCTE-Net consists of two modules: the Temporal-Channel Focus module, which learns a temporal-channel focus matrix to identify the most important feature representations, and the Dynamic Channel Topology Attention module, which dynamically learns spatial topological features, and fuses them with an attention mechanism to model long-distance channel-wise topology. We conduct experiments on NTU RGB+D, NTU RGB+D 120, and FineGym datasets. TCTE-Net shows state-of-the-art performance compared to CNN-based methods and achieves superior performance compared to GCN-based methods. The code is available at https://github.com/aikuniverse/TCTE-Net. |
| 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/173211/57296 |
| 专题 | 紫东太初大模型研究中心_大模型计算 |
| 通讯作者 | Jinzhao Luo |
| 作者单位 | 1.Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Wuhan AI Research 4.The Peng Cheng Laboratory |
| 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 | Jinzhao Luo,Lu Zhou,Guibo Zhu,et al. Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition[C],2023. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| PRCV2023-Temporal-Ch（355KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
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