Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Yang Yi

Xiaoming Yu

Jilin Li

Chengjie Wang

Feiyue Huang

Ying Tai

Yabiao Wang

Keke He

Juanhui Tu

Jinlong Peng

Yi Zhou

Zhengkai Jiang

Feng Li

Chenghao Liu

Xiaoxiang Zuo

Shijie Zhao

Kun Jin

Xu Hao

Shi Jie Zhao

Xiao Xiang Zuo

Yu Tao

Kaijie Cai

Li Lu Xin

In the field of artificial intelligence technologies, a gaze correction method and apparatus for a face image, a device, a computer-readable storage medium, and a computer program product are provided. The method includes: acquiring an eye image from a face image; determining an eye movement flow field based on the eye image and a target gaze direction, the target gaze direction being a gaze direction to which an eye gaze in the eye image is to be corrected; adjusting a pixel position in the eye image based on the eye movement flow field, to obtain a corrected eye image; and generating a face image with a corrected gaze based on the corrected eye image.

Gaze correction method and apparatus for face image, device, computer-readable storage medium, and computer program product face image

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. The image gaze correction method includes: acquiring a to-be-corrected eye image from a to-be-corrected image, generating, based on the to-be-corrected eye image, an eye motion flow field and an eye contour mask, the eye motion flow field being used for adjusting a pixel position in the to-be-corrected eye image, and the eye contour mask being used for indicating a probability that the pixel position in the to-be-corrected eye image belongs to an eye region, performing, based on the eye motion flow field and the eye contour mask, gaze correction processing on the to-be-corrected eye image to obtain a corrected eye image, and generating a gaze corrected image based on the corrected eye image.

Image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

A plurality of groups of portrait components and a region in which the plurality of groups of portrait components are located are recognized from a first image, each group of portrait components corresponding to one human body, and a target region that includes a human face is determined in the region in which the plurality of groups of portrait components are located, to blur regions other than the target region in the first target image. A target group of portrait components including the human face is recognized from the first image, so that the target region in which the target group of portrait components is located is determined as a foreground region, and limbs of other people without a human face are determined as a background region. The disclosed system and method improve the accuracy of recognizing a foreground person and reducing incorrect detection in portrait recognition.

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

In a video processing method, a video frame sequence comprising a moving target is obtained. According to the video frame sequence and by processing circuitry of a neural network model, a motion state feature representing the moving target is obtained. The motion state feature is expressed in a time sequence of the video frame sequence. A result of matching between the motion state feature of the moving target and a motion state feature of a designated target is obtained. The moving target is determined as the designated target in response to a similarity between the motion state feature of the moving target and the motion state feature of the designated target being greater than a threshold. According to the motion state feature of the moving target and by using a classifier, a probability that the moving target pertains to an action category corresponding to the designated target is obtained.

Video processing method and apparatus for action recognition

A hand key-point recognition model training method is provided for a model training device. The method includes: converting a sample virtual image into an emulation image through a Cycle-GAN model, the sample virtual image being an image generated through three-dimensional modeling, and the sample virtual image including key-point coordinates corresponding to hand key-points, and the emulation image being used for emulating an image acquired in a real scenario; extracting a hand image in the emulation image; and training a hand key-point recognition model according to the hand image in the emulation image and the key-point coordinates, the hand key-point recognition model being used for outputting hand key-point coordinates of a hand in a real image according to the inputted real image.

Hand key point recognition model training method, hand key point recognition method and device

Embodiments of the disclosure provide an image processing method and apparatus, a computer-readable medium, and an electronic device. The image processing method includes: extracting a feature map of a target image; dividing the feature map into target regions; determining weights of the target regions according to feature vectors of the target regions; and generating a feature vector of the target image according to the weights of the target regions and the feature vectors of the target regions.

Image processing method and apparatus, computer-readable medium, and electronic device

A method and apparatus are disclosed for identifying malicious software in the technical field of computers. The method includes: obtaining, according to a source code of to-be-detected software, a function call diagram of the software; generating a feature sequence of the software according to the function call diagram, the feature sequence including an eigenvalue of at least one feature, the feature being a function in a preset function library, and the eigenvalue of the feature being a quantity of times of calling of the function by the software; and identifying whether the software is malicious software according to the feature sequence and a random forest, the random forest including at least one decision tree, and the decision tree including reference eigenvalues of multiple features. The apparatus includes: an obtaining module, a generation module, and an identification module. The present disclosure may improve identification accuracy.

Method and apparatus for identifying malicious software

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Shenzhen, China

Yang Yi