Abstract: An image processing method comprises training an initial network to obtain a first network. The method also comprises training the first network to obtain a second network. The method also comprises training the second network to obtain a third network. The method also comprises processing an original image according to the third network in response to receiving an image processing instruction, to obtain a plurality of target images from the original image.

Abstract: A method of video deblurring by an electronic device is described. The processing circuitry of the electronic device acquires N continuous image frames from a video clip the N being a positive integer, and the N continuous image frames including a blurry image frame to be processed. The processing circuitry of the electronic device performs three-dimensional (3D) convolution processing on the N continuous image frames with a generative adversarial network model, to acquire spatio-temporal information corresponding to the blurry image frame.

Abstract: A molten droplet printing system and method can provide molten droplets without surface contact at the time of generation.

Abstract: An image recognition method is provided, including: obtaining a target video including a target object; extracting a target video frame image from the target video; generating a key point video frame sequence comprised of a plurality of key point video frames according to key point information of the object and a plurality of video frames in the target video; extracting dynamic timing feature information of the key point video frame sequence; extracting static structural feature information of the target video frame image; and recognizing an attribute type corresponding to the target object in the target video according to the dynamic timing feature information of the key point video frame sequence and the static structural feature information of the target video frame image.

Abstract: Provided is a video processing method, including: obtaining a to-be-processed video and generating a first gait energy diagram, the to-be-processed video including an object with a to-be-recognized identity; obtaining a second gait energy diagram, the second gait energy diagram being generated based on a video including an object with a known identity; inputting the first gait energy diagram and the second gait energy diagram into a deep neural network; extracting respective identity information of the first gait energy diagram and the second gait energy diagram, and determining a fused gait feature vector from gait feature vectors of the first gait energy diagram and the second gait energy diagram; and calculating a similarity based on at least the fused gait feature vector. The identity information of the first gait energy diagram includes gait feature vectors, and the identity information of the second gait energy diagram includes gait feature vectors.

Abstract: An image processing method comprises training an initial network to obtain a first network. The method also comprises training the first network to obtain a second network. The method also comprises training the second network to obtain a third network. The method also comprises processing an original image according to the third network in response to receiving an image processing instruction, to obtain a plurality of target images from the original image.

Abstract: Embodiments of this application disclose a microexpression-based image recognition method and apparatus, and a related device.

Abstract: A method of video deblurring by an electronic device is described. The processing circuitry of the electronic device acquires N continuous image frames from a video clip the N being a positive integer, and the N continuous image frames including a blurry image frame to be processed. The processing circuitry of the electronic device performs three-dimensional (3D) convolution processing on the N continuous image frames with a generative adversarial network model, to acquire spatio-temporal information corresponding to the blurry image frame.

Abstract: Provided is a video processing method, including: obtaining a to-be-processed video and generating a first gait energy diagram, the to-be-processed video including an object with a to-be-recognized identity; obtaining a second gait energy diagram, the second gait energy diagram being generated based on a video including an object with a known identity; inputting the first gait energy diagram and the second gait energy diagram into a deep neural network; extracting respective identity information of the first gait energy diagram and the second gait energy diagram, and determining a fused gait feature vector from gait feature vectors of the first gait energy diagram and the second gait energy diagram; and calculating a similarity based on at least the fused gait feature vector. The identity information of the first gait energy diagram includes gait feature vectors, and the identity information of the second gait energy diagram includes gait feature vectors.

Abstract: An image recognition method is provided, including: obtaining a target video including a target object; extracting a target video frame image from the target video; generating a key point video frame sequence comprised of a plurality of key point video frames according to key point information of the object and a plurality of video frames in the target video; extracting dynamic timing feature information of the key point video frame sequence; extracting static structural feature information of the target video frame image; and recognizing an attribute type corresponding to the target object in the target video according to the dynamic timing feature information of the key point video frame sequence and the static structural feature information of the target video frame image.