# Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition
###### Abstract  
Research on few-shot action recognition has received widespread attention recently. However, there are some blind spots in the current research: (1) The prevailing practice in many models is to assign uniform weights to all samples; nevertheless, such an approach may yield detrimental consequences for the model in the presence of high-noise samples. (2) Samples with similar features but different classes make it difficult for the model to be distinguished. (3) Skeleton data harbors rich temporal features, but most encoders face challenges in effectively extracting them. In response to these challenges, this study introduces a reconstructed prototype network (RC-PN) based on a prototype network framework and a novel spatiotemporal encoder. The RC-PN comprises two enhanced modules: Sample coefficient reconstruction (SCR) and a reconstruction loss function (\(L_{RC}\)). SCR leverages cosine similarity between samples to reassign sample weights, thereby generating prototypes robust to noise interference and more adept at conveying conceptual essence. Simultaneously, the introduction of \(L_{RC}\) enhances the feature similarity among samples of the same class while increasing feature distinctiveness between different classes. In the encoder aspect, this study introduces a novel spatiotemporal convolutional encoder called CDC-TAGCN. The temporal convolution operator is redefined in CDC-TAGCN. The vanilla temporal convolution operator can only capture the surface-level characteristics of action samples. Drawing inspiration from differential convolution (CDC), this research enhances TCN to CDC-TGCN. CDC-TGCN allows for the fusion of discrepant features from action samples into the features extracted by the vanilla convolutional operator. Extensive feasibility and ablation experiments are performed on the skeleton action dataset NTU-RGB + D 120 and Kinetics and compared with recent research. few-shot; skeleton-based action recognition; prototype network; CDC-TAGCN +
Footnote †: journal: Medical  
datasets. These prototypes can be viewed as cluster centers. (c) A distance measurement module: unlike traditional classification models, models for few-shot learning require distance measurement to gauge the proximity between a query sample and prototypes. The closer the distance, the more similar they are. (d) A loss function module: it calculates the deviation in each feedforward and updates the parameters to bring the model closer to an ideal state. This paper collectively terms modules b, c, and d as the prototype network module, while treating the encoder as a standalone primary module. Existing prototype networks have some limitations. In most cases, researchers generate prototypes for each class by computing the mean of the corresponding support sample features. However, this approach overlooks the potential disparities in quality among the samples. In data acquisition, each sample inevitably incurs some degree of noise interference. The averaging operation treats all samples as having equal weight coefficients. Constructing prototypes using highly noisy samples alongside other samples could lead to the deviation of prototypes from their ideal feature representation. This study proposes an improved prototype network, the reconstructed prototype network (RC-PN), which employs the cosine similarity coefficient reconstruction (SCR) approach to reassign the weights of samples so that prototypes can better encapsulate class characteristics. RC-PN not only alters the prototype's representation but also considers the problem of misclassification of query samples due to minimal differences in the characteristics of some action classes. Considering the above issue, this study proposes a loss function \(L_{RC}\). \(L_{RC}\) amplifies the differences between prototypes of different classes with minimal differences while reducing the dissimilarity between samples within the same class. Lastly, sample labels are determined by nearest-neighbor calculations, thus requiring a distance metric.