基于扩散模型和交叉注意力机制的骨骼点动作识别方法OA

To address the problems of incomplete motion information caused by occlusion or missing joints in skele-ton-based action recognition,as well as the limited generalization ability of models with few-label conditions,a skeleton-based action recognition method DCMAE was proposed,which integrated a diffusion model with a cross-at-tention mechanism.Within a self-supervised learning framework,a spatio-temporal masking strategy was adopted,where the diffusion model learned the global distribution characteristics of motion sequences during the denoising process to improve classification accuracy under data-missing conditions.In the decoding stage,the cross-attention mechanism introduced encoder features to achieve spatio-temporal information interaction and guidance,thereby en-hancing the model's generalization ability in few-label conditions.Experiments conducted on the NTU RGB+D 60 and NTU RGB+D 120 datasets showed that the proposed method could achieve accuracy improvements of up to 14.9 percentage points and 3.0 percentage points,respectively,over SkeletonMAE with data-missing conditions and few-label conditions.The proposed method effectively enhanced the robustness of skeleton-based action recog-nition models to data-missing and few-label data,providing a new perspective for self-supervised action recognition research.