Abstract: With the in-depth research of deep learning technology,its application and development in the field of behavior recognition have been greatly promoted.Current research on behavior recognition based on deep learning usually requires a large training data set.But when facing practical applications,new users will inevitably run into personalization issues.This means that even while performing the same activity,different people may use training data sets differently.Existing solutions cannot guarantee to achieve the expected accuracy when dealing with new users.Besides,these models would also be impractical to deploy when gathe-ring training data for new users.Facing this problem,small-sample learning can achieve better results by using only a small number of samples.This means that in the behavior recognition problem,each new user can be classified using a little training data.In this paper,a MAML-M model is proposed by combining few-shot learning and behavior recognition algorithms.Firstly,an optimization-based meta-learning method is adopted to divide the dataset according to users and construct multiple user tasks for trai-ning and testing.Meanwhile,the MAML method and the memory module based on the attention mechanism are introduced into the MAML-M model,which finally improves the ability of the model network to extract and summarize data features.Through experiment on MEx dataset,the proposed MAML-M model shows better performances under small sample sets.

Key words: Human behavior recognition, Small sample learning, Meta-learning, Attention mechanism