Few-Shot Action Recognition with A Transductive Maximum Margin Classifier

Few-shot action recognition aims to train a classifier that can generalize well when just a small number of labeled videos per class are given. We introduce a transductive maximum margin classifier for few-shot action recognition, which leverages the unlabeled query videos to improve the recognition performance in the test task. The basic idea of the classical maximum margin classifier is to search for a classifier with the largest geometric margin so that training data can be correctly classified. Due to the insufficient number of labeled videos in the support set, it is challenging to find such a classifier with good generalization ability. We observe that exploring the geometric relationship between the separating hyperplane of the classifier and the feature vectors of the query videos can bring improvements to the classifier. In order to improve data utilization efficiency in the few-shot setting, the class prototypes are also treated as examples, which participate in the iterative training process of the model. Experimental results on two action recognition datasets including Kinetics and Something-Something V2 show that our method achieves state-of-the-art performance.