Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Kaihao Zhang

Wenhan Luo

Yan Zhang

Lin Ma

Hongdong Li

Embodiments of this application disclose a microexpression-based image recognition method and apparatus, and a related device. The method includes: obtaining an original expression image belonging to a first expression type, and inputting the original expression image into an image augmentation model; the original expression image belonging to the first expression type being an image including a microexpression; the image augmentation model being obtained by training with a sample expression image belonging to the first expression type and a sample expression images belonging to a second expression type; augmenting, in the image augmentation model, an expression feature of the microexpression in the original expression image to obtain a target expression image belonging to the second expression type; recognizing an expression attribute type corresponding to the target expression image, and determining the expression attribute type corresponding to the target expression image as an expression attribute type corresponding to the original expression image.

Microexpression-based image recognition method and apparatus, and related device

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.

Image processing method and apparatus, computer device, and storage medium

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. The spatio-temporal information includes spatial feature information of the blurry image frame, and temporal feature information between the blurry image frame and a neighboring image frame of the N continuous image frames The processing circuitry of the electronic device performs deblurring processing on the blurry image frame by using the spatio-temporal information corresponding to the blurry image frame through the generative adversarial network model, to output a sharp image frame.

Video deblurring method and apparatus, storage medium, and electronic apparatus

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.

Image recognition method and apparatus, terminal, and storage medium

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.

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Shenzhen, China

Kaihao Zhang