Video data processing method and device

Abstract

Embodiments of the present application provide a video data processing method and device, which relate to the technical field of network media, and can use a deep learning algorithm to process video data; including: clustering video tags in the acquired video tag set class to obtain K label clusters; obtain the sample video category and sample video label corresponding to the sample video data, and determine the sample cluster class identifier of the sample video data according to the label cluster to which the sample video label belongs; output the sample through the initial video multimodal model The sample multimodal features corresponding to the video data; input the sample multimodal features to N classification components, and output N classification results corresponding to the sample video data through the N classification components; according to the N classification results, sample video categories, samples Video labels and sample cluster class identifiers are used to train the initial video multimodal model. By using the embodiments of the present application, the effect of video multimodal representation learning can be improved.

Description

technical field [0001] The present application relates to the technical field of network media, in particular to a video data processing method and device. Background technique [0002] In the context of Internet big data, it is usually necessary to process and analyze specific data and extract useful information from the data, that is, to perform representation learning on the data. How to use the massive data on the Internet to mine effective information has attracted widespread attention. At present, the representation learning of video data can adopt a supervised training method. By obtaining the supervision information of video data, the supervision information can be used to guide the classification of video data features; however, the existing supervision information is usually a single label marked by humans. , the video data features learned by using the supervision information are often coarse-grained features, and the predicted classification results of the video ...

AI Technical Summary

Problems solved by technology

At present, the representation learning of video data can adopt a supervised training method. By obtaining the supervision information of video data, the supervision information can be used to guide the classification of video data features; however, the existing supervision information is usually a single label marked by humans. , the video data features learned by using the supervision information are often coarse-grained features, and the predicted classification results of the video data features may be different from the content of the video data itself, which makes the effect of video data representation learning unsatisfactory.

Images