Systems and techniques that facilitate few-shot temporal action localization based on graph convolutional networks are provided. A graph component can generate a graph that models a support set of temporal action classifications. Nodes of the graph can correspond to respective temporal action classifications in the support set. Edges of the graph can correspond to similarities between the respective temporal action classifications. A convolution component can perform a convolution on the graph, such that the nodes of the graph output respective matching scores indicating levels of match between the respective temporal action classifications and an action to be classified. An instantiation component can input into the nodes respective input vectors based on a proposed feature vector representing the action to be classified. The respective temporal action classifications can correspond to respective example feature vectors, and the respective input vectors can be concatenations of the respective example feature vectors and the proposed feature vector.
NEW FRAMEWORK FOR FEW-SHOT TEMPORAL ACTION LOCALIZATION