Model processing method, related equipment, storage medium and computer program product

Abstract

The present application discloses a model processing method, related equipment, storage medium and computer program product. The method includes: extracting the first video segment and the second video segment from the sample video, and the distance between the first video segment and the second video segment The spatio-temporal overlap rate is equal to the reference spatio-temporal overlap rate; the video processing model is called to extract the spatio-temporal feature of the first video clip as the first spatio-temporal feature, and extract the spatio-temporal feature of the second video clip as the second spatio-temporal feature; according to the first spatio-temporal feature and the second spatio-temporal feature Two spatio-temporal features, predict the spatio-temporal overlap rate between the first video segment and the second video segment, and obtain the spatio-temporal overlap rate prediction result; calculate the model loss value based on the reference spatio-temporal overlap rate and the spatio-temporal overlap rate prediction result; according to the reduction model The direction of the loss value, optimizing the network parameters of the video processing model. This application can enhance the spatio-temporal representation capability of the model, so that the model can construct more accurate video spatio-temporal fingerprints.

Description

Technical field [0001] The present application relates to computer technologies, and particularly relates to the field of artificial intelligence technology, particularly to a method of processing model, related equipment, storage medium, and computer program products. Background technique [0002] With the rapid development of science technology, video has become an important carrier of information daily, more and more people tend to use video to get valid information. With the number of growth spurt video, video type and style of each passing day, there will usually be model of neural network model based approach to optimize the depth of learning to extract the spatial characteristics of each individual video video video as through optimized model fingerprint space to space through video fingerprint to distinguish each video. Wherein the video refers to video fingerprints fingerprint space having only spatial information; is a so-called video fingerprint generated based on the ...

AI Technical Summary

Problems solved by technology

[0003] Practice has shown that the existing model processing method makes the optimized model only have the ability of spatial representation, so that the optimized model can only construct the video spatial fingerprint of the video; but due to the low accuracy of the video spatial fingerprint, it cannot It is very good to distinguish videos with the same scene but different actions. Therefore, how to perform model processing so that the model can construct other types of video fingerprints that are more accurate than video spatial fingerprints has become a current research hotspot.

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