Abstract: The present disclosure relates to an image style transfer method and apparatus, an image style transfer model training method and apparatus, a device, and a medium. The image style transfer model training method includes: acquiring a first number of portrait image samples, training a preset neural network model by using the portrait image samples to determine a portrait image generation model and portrait model parameters; acquiring a second number of style image samples, training the portrait image generation model by using the style image samples to determine style model parameters, the second number being less than the first number; determining transfer model parameters based on the portrait model parameters and the style model parameters; and generating a first image style transfer model based on the transfer model parameters and the preset neural network model.

Abstract: The present disclosure provides a voice prompt system, a voice prompt method for a bridge girder erection unit, a computing device, and a medium. The voice prompt system includes a control circuit and an alarm circuit; the control circuit includes a control power supply, a programmable controller, and relays; the positive electrode of the control power supply is electrically connected with an input end of the programmable controller, an output end of the programmable controller is electrically connected with input ends of the at least two relays, respectively, and output ends of the relays are connected to the negative electrode of the control power supply after being connected in parallel; the alarm circuit includes relay contacts, a wiring terminal block, and an alarm; the wiring terminal block includes at least two high-potential pins and a low-potential pin.

Abstract: Embodiments of the present disclosure relate to an image processing method, an image generating method, an apparatus, a device and a medium. The image processing method may include: acquiring an original image; determining a comic image corresponding to the original image; acquiring line information and color block information of the comic image; determining, according to the line information and the color block information of the comic image, a paper-cut image corresponding to the original image.

Abstract: A method of generating a virtual idol is provided. The method includes: during obtaining of a virtual idol, feature information of a target object may be obtained, and then the feature information of the target object is used as a generation basis for the virtual idol, so that a target virtual face matching with the feature information may be determined from a preset face material library in a targeted manner, and a target motion video matching with the feature information may be determined from a preset motion video library; and then the virtual face and a facial image in the motion video are fused, which may generate the virtual idol for a scenario where the target object is endorsed, so that the virtual idol may subsequently be used to endorse the target object.

Abstract: A method, an electronic device, and a storage medium are disclosed.

Abstract: A method of face anti-spoofing, a device and a storage medium are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology, and can be applied in a face recognition scene. The method includes: acquiring a face image sequence including a plurality of face images; acquiring, based on the face image sequence, a corresponding eye image sequence including a plurality of eye images; recognizing, based on the eye image sequence, a color of an optical spot caused by a pupil in each eye image, so as to obtain a pupil color recognition result; and determining a face anti-spoofing result by using the face image in the face image sequence, in response to the pupil color recognition result indicating that the face image sequence is a sequence of face images captured on site after a face recognition process is started.

Abstract: A computer-implemented method includes: acquiring training data, the training data includes training images for a preset vertical type, and the training images include a first training image containing real data of the preset vertical type and a second training image containing virtual data of the preset vertical type ; building a basic model, the basic model includes a deep learning network, and the deep learning network is configured to recognize the training images to extract text data in the training image; and training the basic model by using the training data to obtain the image recognition model.