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.

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 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 vehicle locating method, an apparatus, an electronic device, a storage medium and a computer program product are provided, and relate to the technical field of intelligent transportation. The method includes: acquiring an image of a current road within an area in which a target vehicle is currently located, and acquiring current lane related information within the area in which the target vehicle is currently located from a map application; 5determining a current road recognition result based on the image of the current road; and determining a lane in which the target vehicle is currently located based on at least one of the current road recognition result and the current lane related information, and taking the lane in which the target vehicle is currently located as the current lane location result for the target vehicle.

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.