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

Abstract

The invention discloses a model processing method, related equipment, a storage medium and a computer program product, and the method comprises the steps: intercepting a first video clip and a second video clip from a sample video, and the space-time overlapping rate between the first video clip and the second video clip is equal to a reference space-time overlapping rate; calling a video processing model, extracting a spatial-temporal feature of the first video clip as a first spatial-temporal feature, and extracting a spatial-temporal feature of the second video clip as a second spatial-temporal feature; according to the first spatiotemporal feature and the second spatiotemporal feature, predicting a spatiotemporal overlapping rate between the first video clip and the second video clip to obtain a spatiotemporal overlapping rate prediction result; calculating a model loss value based on the reference space-time overlapping rate and the space-time overlapping rate prediction result; and optimizing network parameters of the video processing model according to the direction of reducing the loss value of the model. According to the method, the space-time representation capability of the model can be enhanced, so that the model can construct more accurate video space-time fingerprints.

Description

technical field [0001] The present application relates to the field of computer technology, specifically to the field of artificial intelligence technology, and in particular to a model processing method, related equipment, storage media and computer program products. Background technique [0002] With the rapid development of technology, video has gradually become an important carrier of daily information, and people are more and more inclined to use video to obtain effective information. With the explosive growth of the number of videos, the types and styles of videos are changing with each passing day. At present, the neural network model is usually optimized based on deep learning, so that the spatial features of each video can be extracted through the optimized model as the video of each video. Spatial fingerprint to distinguish individual videos by video spatial fingerprint. Wherein, the video spatial fingerprint refers to a video fingerprint with only spatial informa...

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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