Skeleton based action recognitionRe-parameterization Depth-Wise ConvolutionFeature fusionAction recognition algorithms that leverage human skeleton motion data are highly attractive due to their robustness and high information density. Currently, the majority of algorithms in this domain employ graph ...
读书笔记:Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition,程序员大本营,技术文章内容聚合第一站。
ST-GCN。Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. AAAI 2018 开源: https://github.com/yysijie/st-gcn 这是一篇用骨架来做动作识别的文章。 之前的基于骨架的方…
论文题目:Local and global self-attention enhanced graph convolutional network for skeleton-based action recognition 作者&团队:Wu Z, Ding Y, Wan L, et al. 1.合肥大学人工智能与大数据学院 2.安徽大学人工智能学院 发表期刊/会议:Pattern Recognition 中科院分区:SCI 2区 年份、卷号、刊号、页码:2025, 159...
【论文学习】ST-GCN:Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,程序员大本营,技术文章内容聚合第一站。
Our full model, the BlockGCN, establishes new benchmarks in skeleton-based action recognition across all model categories. Its high accuracy and lightweight design, most notably on the large-scale NTU RGB+D 120 dataset, stand as strong validation of the efficacy of ...
Spatial Temporal Graph Convolutional Networks for Skeleton Based Action Recognition 摘要 动态人体骨架模型带有进行动作识别的重要信息,传统的方法通常使用手工特征或者遍历规则对骨架进行建模,从而限制了表达能力并且很难去
In this paper, to construct more powerful binary GCNs, we propose two novel progressive binary GCN (PB-GCN and PB-GCN*) for skeleton-based action recognition, which binarizes the weights of GCN to {-α,+α} as well as activations to [-1,+1]. The framework of the proposed PB-GCN an...
GCN for skeleton-based action recognition 图卷积网络广泛应用于基于骨架的动作识别中,它将人类骨骼序列建模为时空图。ST-GCN是基于GCN的方法的著名baseline,它结合了空间图卷积和交错时间卷积,用于时空建模。在baseline上,adjacency powering用于多尺度建模,而自我注意机制提高了建模能力。尽管GCN在基于骨架的动作识别方面...
In this approach, a GCN is used for skeleton-based action recognition. This impetus has pushed GCN-based methods to the forefront of recognition tasks, cleverly capturing the subtleties of space and time by constructing spatiotemporal graphs. It is worth noting that this type of method ...