Video classification method and device, electronic equipment and storage medium

Abstract

The invention discloses a video classification method and device, electronic equipment and a storage medium, and particularly relates to the technical field of artificial intelligence such as computer vision and deep learning. The specific implementation scheme is as follows: acquiring multiple groups of image frames from a to-be-processed video; performing feature extraction on each group of extracted image frames to determine a feature map corresponding to each group of image frames; determining a weight corresponding to each group of image frames; based on the weight corresponding to each group of image frames, fusing the feature maps to generate a total feature map corresponding to the video; and classifying the videos according to the total feature map to determine category labels corresponding to the videos. Therefore, when the video is classified, not only is the content contained in the image frame in the video considered, but also the effect of the image frame in the video is considered, so that the classification result is more accurate and more reliable.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, specifically to the technical fields of artificial intelligence such as computer vision and deep learning, and in particular to a video classification method, device, electronic equipment, and storage medium. Background technique [0002] With the rapid development of Internet technology, video surveillance, video recommendation, video search, etc. have shown great application potential. Therefore, the correct identification and classification of videos has become one of the most active research topics in the computer vision community. . [0003] The important metrics for evaluating video recognition and classification methods are classification accuracy and inference cost. Nowadays, video recognition has achieved great success in recognition accuracy, but it is still a challenging task due to the huge computational cost. Therefore, how to improve the recognition accuracy witho...

AI Technical Summary

Problems solved by technology

[0003] The important metrics for evaluating video recognition and classification methods are classification accuracy and inference cost. Today, video recognition has achieved great success in recognition accuracy, but it is still a challenging task due to the huge computational cost.

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