Short-Form video classification based on Gate shift module and Semantic embedding

Abstract

Most of the existing video classification methods are based on large-scale data training, which can better realize the recognition and classification of known categories. However, data labelling is cumbersome and most things are unknown. Therefore, the existing video classification methods fall into a data bottleneck. This paper proposes a short video classification method based on GSM and semantic embedding. It uses super large-scale text information to assist the recognition process of the video classification model. This is an important development in the classification effect of knowledge categories. Specifically, this article expands the video classification method, adds category semantic embedding in the video feature extraction process, and trains to continuously fit the word vector of the corresponding category, and then uses semantic similarity to realize the classification of unknown categories. Multi-angle comparative experiments verify the effectiveness of this model, which can achieve good classification of unknown video categories.