Huawei Technologies Co., Limited

Chinese Academy of Sciences

Bailan Feng

Chunfeng Yao

Bing Yu

Zhang Zhang

Kaiqi Huang

Yang Zhou

Ke Zhao Li

Li Qian

Yun Yang

Bo Yan

Xiang Wu

Zhenan Sun

Defeng Li

Ran He

Yibo Hu

Bahetiyaer Bare

Dangwei Li

Houjing Huang

Xiaotang Chen

Songjiang Li

Qiang Rao

Chuanxia Zheng

The present disclosure relates to image processing methods and apparatus. One example image processing method includes obtaining a face image, separately obtaining a left face image and a right face image based on the face image, inputting the left face image into a first target key point convolutional neural network model, outputting coordinates of a first left face key point, inputting the right face image into a second target key point convolutional neural network model, outputting coordinates of a first right face key point, and obtaining coordinates of a face key point of the face image based on the coordinates of the first left face key point and the coordinates of the first right face key point.

Image processing method and apparatus

An image processing method, a related device, and a computer storage medium are provided. The method includes: obtaining a feature intensity image corresponding to a training image, where an intensity value of a pixel in the feature intensity image is used to indicate importance of the pixel for recognizing the training image, and resolution of the training image is the same as resolution of the feature intensity image; and occluding, based on the feature intensity image, a to-be-occluded region in the training image by using a preset window, to obtain a new image, where the to-be-occluded region includes a to-be-occluded pixel, and the new image is used to update an image recognition model. According to the embodiments of the present application, a prior-art problem that a model has low accuracy and relatively poor generalization performance because of limited training data can be resolved.

Image processing method, related device, and computer storage medium

An image super-resolution method includes preprocessing the low-resolution image to obtain a vertical gradient map, a horizontal gradient map, and a luminance map, which are used as three different dimensions of information to constitute a to-be-input feature map, performing size conversion on the to-be-input feature map to obtain an input feature map, performing nonlinear transformation on the input feature map to obtain an input feature map obtained after the nonlinear transformation, and performing weighted processing on the input feature map and the input feature map obtained after the nonlinear transformation, to obtain an output feature map, performing size conversion on the output feature map to obtain a residual map, and combining the low-resolution image and the residual map to obtain a high-resolution image.

Image super-resolution method and apparatus

A method for pedestrian attribute identification and positioning is provided. The method includes: performing feature extraction on a to-be-detected image at a plurality of different abstraction degrees, to obtain a plurality of first feature maps of a pedestrian attribute; performing convolution on the plurality of first feature maps, to obtain a plurality of second feature maps; mapping each second feature map to a plurality of areas (bins) that overlap each other, and performing max pooling on each bin, to obtain a plurality of high-dimensional feature vectors, where the plurality of bins that overlap each other evenly cover each second feature map; processing the plurality of high-dimensional feature vectors into a low-dimensional vector, to obtain an identification result of the pedestrian attribute; and further obtaining a positioning result of the pedestrian attribute based on the plurality of second feature maps and the plurality of high-dimensional feature vectors.

Pedestrian attribute identification and positioning method and convolutional neural network system

Embodiments of this application provide an image reconstruction method and device. The method includes: inputting a first image into a newly constructed super-resolution model to obtain a reconstructed second image, where a resolution of the second image is higher than that of the first image. The newly constructed super-resolution model is obtained by training an initial super-resolution model by using an error loss. The error loss includes a pixel mean square error and an image feature mean square error. The image feature in the image feature mean square error includes at least one of a texture feature, a shape feature, a spatial relationship feature, and an image high-level semantic feature. According to the embodiments of this application, the quality of a reconstructed image can be improved.

Image reconstruction method and device

A method and an apparatus for recognizing a descriptive attribute of an appearance feature include obtaining a location feature of an appearance feature of a target image to determine a location of a part of an object in a preset object model indicated by the appearance feature, where the location feature of the appearance feature indicates the location of the part of the object in the preset object model indicated by the appearance feature, recognizing a target region based on the location feature, where the target region includes the part of the object, performing feature analysis on the target region, recognizing a descriptive attribute of the appearance feature of the object, and determining the location feature of the appearance feature having a local attribute.

Method and apparatus for recognizing descriptive attribute of appearance feature

A method and an apparatus for generating a face rotation image are provided. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, wherein presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, wherein the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.

Method and apparatus for generating face rotation image

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Beijing, China

Bailan Feng