Dynamic skeletal data has been widely studied for human action tasks due to its high-level semantic information and less data than RGB features. However, attention-based previous methods fail to focus on the local grouped joint dependence of the human body, which is vital to distinguishing various actions in fine-grained tasks, such as skeletal action segmentation and recognition. This work proposes spatial focus attention for the fine-grained skeleton-based action tasks. Specifically, we decouple the attention map to enhance the grouped joint dependence adaptively by the decouple probability. To further focus on local grouped dependence, the tree structural attention maps can be built by hierarchical decoupling and guide the model to focus on complementary local dependence in the different leaf nodes. Our proposed approach achieves state-of-the-art performance on fine-grained skeleton-based human action segmentation tasks (MCFS-22) and recognition tasks (FSD-10). Besides, on the coarse-grained dataset (NTU-60), the proposed spatial focus attention also achieves outstanding performance.

中文翻译：

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基于细粒度骨架的动作任务的空间焦点注意力


动态骨骼数据由于其高级语义信息和比 RGB 特征更少的数据而被广泛研究用于人类动作任务。然而，先前基于注意力的方法未能关注人体的局部分组关节依赖性，这对于区分细粒度任务中的各种动作至关重要，例如骨骼动作分割和识别。这项工作提出了基于细粒度骨架的动作任务的空间焦点注意力。具体来说，我们解耦注意力图，通过解耦概率自适应地增强分组联合依赖性。为了进一步关注局部分组依赖性，可以通过分层解耦来构建树结构注意图，并引导模型关注不同叶节点中的互补局部依赖性。我们提出的方法在基于细粒度骨架的人体动作分割任务（MCFS-22）和识别任务（FSD-10）上实现了最先进的性能。此外，在粗粒度数据集（NTU-60）上，所提出的空间焦点注意力也取得了出色的性能。