Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: A semantic extraction method based on a natural language is provided. In a character library based on a single-character granularity, a character of an input natural language is queried, to obtain first semantic vectors representing semantic descriptions of the character. Candidate semantic intentions corresponding to second semantic vectors and weight vectors corresponding to the candidate semantic intentions are obtained, wherein the second semantic vectors are obtained by using a neural network and correspond to different permutations and combinations of the first semantic vectors. A candidate semantic intention is selected as a final semantic intention of the natural language based on a preset condition, and key information is obtained corresponding to the final semantic intention according to a weight vector corresponding to the final semantic intention.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: A semantic extraction method based on a natural language is provided. In a character library based on a single-character granularity, a character of an input natural language is queried, to obtain first semantic vectors representing semantic descriptions of the character. Candidate semantic intentions corresponding to second semantic vectors and weight vectors corresponding to the candidate semantic intentions are obtained, wherein the second semantic vectors are obtained by using a neural network and correspond to different permutations and combinations of the first semantic vectors. A candidate semantic intention is selected as a final semantic intention of the natural language based on a preset condition, and key information is obtained corresponding to the final semantic intention according to a weight vector corresponding to the final semantic intention.