Abstract: A video editing method, apparatus and storage medium are provided. The method includes obtaining an object, the object including one or more images; determining a content element of the object for video editing, the content element having a content type identifier; determining a material set identifier corresponding to the content type identifier according to a first behavior tree logic; determining a video editing material set corresponding to the material set identifier; and obtaining an edited video according to the content element and the video editing material set.

Abstract: Embodiments of this application disclose a headword extraction method performed by a computer device. The method includes: obtaining a sentence feature of a target sentence and word features of a plurality of words in the target sentence; extracting semantics of the sentence feature of the target sentence to obtain a semantic feature of the target sentence, and extracting semantics of the word feature of each word to obtain a semantic feature of the word; obtaining a respective matching degree between the semantic feature of each of the plurality of words and the semantic feature of the target sentence; and determining, among the plurality of words in the target sentence, a word with a largest corresponding matching degree as a headword of the target sentence. The method can improve the accuracy of headwords extracted from the target sentence.

Abstract: An image recognition method is provided. The method includes obtaining predicted locations of joints of a target person in a to-be-recognized image based on a joint prediction model, where the joint prediction model is pre-constructed by: obtaining a plurality of sample images; inputting training features of the sample images and a body model feature to a neural network and obtaining predicted locations of joints in the sample images outputted by the neural network; updating a body extraction parameter and an alignment parameter; and inputting the training features of the sample images and the body model feature to the neural network to obtain the joint prediction model.

Abstract: A method includes: obtaining a first image including a target item; selecting a plurality of reference images corresponding to the first image from a database; performing word segmentation on item text information corresponding to the plurality of reference images to obtain a plurality of words; and extracting a key word meeting a reference condition from the plurality of words, and determining the extracted key word as an item name of the target item.

Abstract: An image processing method and a related apparatus are provided. The method is applied to an image processing device, and includes: obtaining an original image, the original image including a foreground object; extracting a foreground region from the original image through a deep neural network; identifying pixels of the foreground object from the foreground region; forming a mask according to the pixels of the foreground object, the mask including mask values corresponding to the pixels of the foreground object; and extracting the foreground object from the original image according to the mask.

Abstract: A video editing method, apparatus and storage medium are provided. The method includes obtaining an object, the object including one or more images; determining a content element of the object for video editing, the content element having a content type identifier; determining a material set identifier corresponding to the content type identifier according to a first behavior tree logic; determining a video editing material set corresponding to the material set identifier; and obtaining an edited video according to the content element and the video editing material set.

Abstract: An augmented reality processing method is provided for a terminal. The method includes: obtaining a plurality of frames of images, comprising a first image and a second image, which is a frame of an image immediately following the first image; obtaining a key point set of a first object in the first image; obtaining, through a neural network model, first pose key point sets respectively corresponding to a plurality of objects in the second image; determining a second pose key point set of the first object in the second image according to the key point set and a motion trend of the first object; using a target first pose key point set as a key point set of the first object in the second image; and generating an augmented information image according to the key point set of the first object in the second image.

Abstract: A video image processing method and apparatus is described. The method includes obtaining a first key point of an Mth frame image in a video. The method further includes performing weighted smoothing on the first key point of the Mth frame image according to first key points in a historical key point queue and weights in a first target weight queue, to obtain a target key point. The historical key point queue includes a first key point corresponds to each frame image in N frame images. The N frame images are images before the Mth frame image, N>0. The weights in the first target weight queue corresponding to the first key points in the historical key point queue, A weight corresponding to a first key point of an (M-a)th frame image being greater than or equal to a weight corresponding to a first key point of an (M-b)th frame image, and a<b. The method further includes adjusting the Mth frame image according to the target key point.

Abstract: Embodiments of the present disclosure provide an image recognition method and apparatus, an electronic device, and a readable storage medium. The method for image recognition includes the steps of obtaining a to-be-recognized feature of a to-be-recognized image, the to-be-recognized image comprising a target person; obtaining a preset body model feature of a body frame image, the body model feature comprising locations of joints in the body frame image; inputting the to-be-recognized feature and the body model feature to a pre-constructed joint prediction model, the joint prediction model being obtained through training a neural network by using minimum respective differences between real locations of joints in a sample image and predicted locations of the corresponding joints as a training objective; and obtaining predicted locations of joints of the target person in the to-be-recognized image based on the joint prediction model.

Abstract: An augmented reality processing method is provided for a terminal. The method includes: obtaining a plurality of frames of images, comprising a first image and a second image, which is a frame of an image immediately following the first image; obtaining a key point set of a first object in the first image; obtaining, through a neural network model, first pose key point sets respectively corresponding to a plurality of objects in the second image; determining a second pose key point set of the first object in the second image according to the key point set and a motion trend of the first object; using a target first pose key point set as a key point set of the first object in the second image; and generating an augmented information image according to the key point set of the first object in the second image.

Abstract: An image processing method and a related apparatus are provided. The method is applied to an image processing device, and includes: obtaining an original image, the original image including a foreground object; extracting a foreground region from the original image through a deep neural network; identifying pixels of the foreground object from the foreground region; forming a mask according to the pixels of the foreground object, the mask including mask values corresponding to the pixels of the foreground object; and extracting the foreground object from the original image according to the mask.

Abstract: A video image processing method and apparatus is described. The method includes obtaining a first key point of an Mth frame image in a video. The method further includes performing weighted smoothing on the first key point of the Mth frame image according to first key points in a historical key point queue and weights in a first target weight queue, to obtain a target key point. The historical key point queue includes a first key point corresponds to each frame image in N frame images. The N frame images are images before the Mth frame image, N>0. The weights in the first target weight queue corresponding to the first key points in the historical key point queue, A weight corresponding to a first key point of an (M-a)th frame image being greater than or equal to a weight corresponding to a first key point of an (M-b)th frame image, and a<b. The method further includes adjusting the Mth frame image according to the target key point.

Abstract: Embodiments of this application provide a neural network model deployment method, a prediction method and a device. The described features can implement deployment of a neural network model to improve the universality of the deployment of the neural network model to the terminal device by obtaining a layer definition and an operation parameter of each network layer of an initial neural network model, executing a target network layer corresponding to the network layers, applying relational connections amongst the target network layers using a net class, converting the operation parameters into a preset format, obtaining a target operation parameter based on the preset format, loading a corresponding target operation parameter in the target network layer, and obtaining a target neural network model based on the target operation parameter.