Patent ID: 11967152
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. A video classification model construction method, performed by a computer device, the method comprising:
obtaining training dataset, the training dataset comprising a plurality of video frames;
inputting the training dataset into a to-be-trained classification model, the to-be-trained classification model comprising a plurality of convolutional layer groups and one output layer group, at least one of the convolutional layer groups comprising a non-local module and each convolutional layer group comprising at least one bottleneck module, the bottleneck module and the non-local module being alternately arranged when the bottleneck module and the non-local module are present in a same convolutional layer group;
performing depthwise separable convolution on feature maps corresponding to the plurality of video frames by using the bottleneck module, and performing channel doubling on feature maps obtained after the depthwise separable convolution to obtain a first feature map group;
inputting the first feature map group into the non-local module, and calculating, by the non-local module, correspondences between spatial positions in each feature map as long-distance dependency features, to output a second feature map group comprising the long-distance dependency features of the plurality of video frames, the long-distance dependency features being used to represent association relationships between spatial positions of the plurality of video frames;
outputting the long-distance dependency features to the output layer group, to generate predicted classification results of the plurality of video frames; and
training the to-be-trained classification model through a prediction loss function until a parameter in the to-be-trained classification model converges, to obtain a video classification model.