Abstract: This application discloses a video classification method performed by a computer device. The method includes: obtaining an image frame sequence corresponding to a to-be-classified video file; obtaining an appearance information feature sequence corresponding to the image frame sequence by using an image classification network model, the appearance information feature sequence including T appearance information features; obtaining a motion information feature sequence corresponding to the appearance information feature sequence by using a motion prediction network model, the motion information feature sequence including T motion information features, and the motion prediction network model being configured to predict the motion information features corresponding to the appearance information features; and determining a video classification result of the to-be-classified video file according to the appearance information feature sequence and the motion information feature sequence.

Abstract: The present disclosure describes a method, apparatus, and storage medium for classifying a multimedia resource. The method includes obtaining a multimedia resource. extracting a plurality of features of the multimedia resource; clustering the plurality of features to obtain at least one cluster set, and determining cluster description information of each cluster set, the each cluster set comprising at least one feature of the multimedia resource; determining at least one piece of target feature description information of the multimedia resource based on the cluster description information of each cluster set, each piece of target feature description information being used for representing an association between one piece of cluster description information and the remaining cluster description information; and classifying the multimedia resource based on the at least one piece of target feature description information of the multimedia resource, to obtain a classification result of the multimedia resource.

Abstract: This application discloses a video classification method performed by a computer device. The method includes: obtaining an image frame sequence corresponding to a to-be-classified video file; obtaining an appearance information feature sequence corresponding to the image frame sequence by using an image classification network model, the appearance information feature sequence including T appearance information features; obtaining a motion information feature sequence corresponding to the appearance information feature sequence by using a motion prediction network model, the motion information feature sequence including T motion information features, and the motion prediction network model being configured to predict the motion information features corresponding to the appearance information features; and determining a video classification result of the to-be-classified video file according to the appearance information feature sequence and the motion information feature sequence.

Abstract: A video classification method is provided for a computer device. The method includes obtaining a to-be-processed video, where the to-be-processed video has a plurality of video frames, and each video frame corresponds to one time feature; and sampling the to-be-processed video according to a time-feature sampling rule, and obtaining at least one video frame feature sequence. The time-feature sampling rule is a correspondence between time features and video frame feature sequences. The method also includes processing the video frame feature sequence by using a first neural network model, to obtain a feature representation result corresponding to the video frame feature sequence, where the first neural network model is a recurrent neural network model; and processing the feature representation result by using a second neural network model, to obtain a prediction result corresponding to the video frame feature sequence.

Abstract: The present disclosure describes a method, apparatus, and storage medium for classifying a multimedia resource. The method includes obtaining a multimedia resource. extracting a plurality of features of the multimedia resource; clustering the plurality of features to obtain at least one cluster set, and determining cluster description information of each cluster set, the each cluster set comprising at least one feature of the multimedia resource; determining at least one piece of target feature description information of the multimedia resource based on the cluster description information of each cluster set, each piece of target feature description information being used for representing an association between one piece of cluster description information and the remaining cluster description information; and classifying the multimedia resource based on the at least one piece of target feature description information of the multimedia resource, to obtain a classification result of the multimedia resource.

Abstract: A video classification method is provided for a computer device. The method includes obtaining a to-be-processed video, where the to-be-processed video has a plurality of video frames, and each video frame corresponds to one time feature; and sampling the to-be-processed video according to a time-feature sampling rule, and obtaining at least one video frame feature sequence. The time-feature sampling rule is a correspondence between time features and video frame feature sequences. The method also includes processing the video frame feature sequence by using a first neural network model, to obtain a feature representation result corresponding to the video frame feature sequence, where the first neural network model is a recurrent neural network model; and processing the feature representation result by using a second neural network model, to obtain a prediction result corresponding to the video frame feature sequence.