Abstract: An image generation method, apparatus, and device, and a medium are provided. The method includes: acquiring a first image, keeping a target attribute in the first image unchanged, and editing other attributes in the first image; on the basis of the target attribute and the edited other attributes, generating a second image.

Abstract: A face image processing method and apparatus, and a device and a medium are provided. The method includes: acquiring a first feature map and a second feature map of a current layer, and generating a plurality of original makeup feature regions corresponding to a plurality of face parts according to the first feature map, generating a plurality of reference makeup feature regions corresponding to the plurality of face parts according to the second feature map, performing makeup migration calculation on each of the original makeup feature regions and a corresponding reference makeup feature region to acquire a plurality of candidate makeup feature regions, stitching the plurality of candidate makeup feature regions to generate a target feature map, and judging whether the target feature map satisfies a preset decoding condition.

Abstract: The embodiment of the disclosure discloses a method and apparatus for rendering a virtual avatar, an electronic device and a storage medium, and the method includes: segmenting a target image to obtain at least one first object and second objects respectively corresponding to each first object; the at least one first object respectively corresponding to at least one pre-constructed first spatial building block avatar; constructing, based on a mapping relationship between the at least one first object and the at least one first spatial building block avatar respectively corresponding to the at least one first object, second spatial building block avatars of second objects respectively corresponding to each first object; and rendering each of the first spatial building block avatars and each of the second spatial building block avatars.

Abstract: Embodiments of the disclosure disclose a color classification method and apparatus, an electronic device and a storage medium. The color classification method including: determining, according to a first color numerical value of a color to be classified under a first color space, an initial category to which the color to be classified belongs, wherein the first color space includes a hue dimension; taking at least one sub-color category under the initial category as at least one candidate category; determining a target category of the color to be classified from the at least one candidate category according to a similarity of a second color numerical value of the color to be classified in a second color space with a third color numerical value of each of the at least one candidate category in the second color space.

Abstract: A method of generating a stylized 3D avatar is provided. The method includes receiving an input image of a user, generating, using a generative adversarial network (GAN) generator, a stylized image, based on the input image, and providing the stylized image to a first model to generate a first plurality of parameters. The first plurality of parameters include a discrete parameter and a continuous parameter. The method further includes providing the stylized image and the first plurality of parameters to a second model that is trained to generate an avatar image, receiving, from the second model, the avatar image, comparing the stylized image to the avatar image, based on a loss function, to determine an error, updating the first model to generate a second plurality of parameters that correspond to the first plurality of parameters, based on the error, and providing the second plurality of parameters as an output.

Abstract: Methods and systems for enlarging a stylized region of an image are disclosed that include receiving an input image, generating, using a first generative adversarial network (GAN) generator, a first stylized image, based on the input image, normalizing the input image, generating, using a second generative adversarial network (GAN) generator, a second stylized image, based on the normalized input image, blending the first stylized image and the second stylized image to obtain a third stylized image, and providing the third stylized image as an output.

Abstract: The present disclosure relates to an image generation method, apparatus, and device, and a medium. The method comprises: acquiring a first image, keeping a target attribute in the first image unchanged, and editing other attributes in the first image; on the basis of the target attribute and the edited other attributes, generating a second image, so as to obtain the second image having the target attribute unchanged and other attributes changed. Therefore, the effect of quick image generation and improved image diversification of FIG. 5 can be achieved, such that during model training, the balance of training samples is improved, so as to improve the performance of the model.

Abstract: The present disclosure provides a training method and device for an image identifying model, and an image identifying method. The training method comprises: obtaining image samples of a plurality of categories; inputting image samples of each category into a feature extraction layer of the image identifying model to extract a feature vector of each image sample; calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category; establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information; obtaining augmented sample features of the each category based on the augmented distribution function; and inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning.

Abstract: A face image processing method and apparatus, and a device and a medium are provided. The method includes: acquiring a first feature map and a second feature map of a current layer, and generating a plurality of original makeup feature regions corresponding to a plurality of face parts according to the first feature map, generating a plurality of reference makeup feature regions corresponding to the plurality of face parts according to the second feature map, performing makeup migration calculation on each of the original makeup feature regions and a corresponding reference makeup feature region to acquire a plurality of candidate makeup feature regions, stitching the plurality of candidate makeup feature regions to generate a target feature map, and judging whether the target feature map satisfies a preset decoding condition.

Abstract: A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.

Abstract: A method of generating a stylized 3D avatar is provided. The method includes receiving an input image of a user, generating, using a generative adversarial network (GAN) generator, a stylized image, based on the input image, and providing the stylized image to a first model to generate a first plurality of parameters. The first plurality of parameters include a discrete parameter and a continuous parameter. The method further includes providing the stylized image and the first plurality of parameters to a second model that is trained to generate an avatar image, receiving, from the second model, the avatar image, comparing the stylized image to the avatar image, based on a loss function, to determine an error, updating the first model to generate a second plurality of parameters that correspond to the first plurality of parameters, based on the error, and providing the second plurality of parameters as an output.

Abstract: An image processing method includes acquiring a set of image samples for training an attribute recognition model, wherein the set of image samples includes a first subset of image samples with category labels and a second subset of image samples without category labels; training a sample prediction model using the first subset of image samples, and predicting categories of the image samples in the second subset of image samples using the trained sample prediction model; determining a category distribution of the set of image samples based on the category labels of the first subset of image samples and the predicted categories of the second subset of image samples; and acquiring a new image sample if the determined category distribution does not conform to the expected category distribution, and adding the acquired new image sample to the set of image samples.

Abstract: Methods and systems for enlarging a stylized region of an image are disclosed that include receiving an input image, generating, using a first generative adversarial network (GAN) generator, a first stylized image, based on the input image, normalizing the input image, generating, using a second generative adversarial network (GAN) generator, a second stylized image, based on the normalized input image, blending the first stylized image and the second stylized image to obtain a third stylized image, and providing the third stylized image as an output.

Abstract: The present disclosure relates to method, apparatus and storage medium for object attribute classification model training. There proposes a method of training a model for object attribute classification, comprising steps of: acquiring binary class attribute data related to a to-be-classified attribute on which an attribute classification task is to be performed, wherein the binary class attribute data includes data indicating whether the to-be-classified attribute is “Yes” or “No” for each of at least one class label; and pre-training the model for object attribute classification based on the binary class attribute data.

Abstract: The present disclosure relates to an image annotating method, classification method and machine learning model training method, and to the field of computer technologies. The image annotating method includes: generating an image tag vector of image to be annotated, according to a plurality of attributes for image annotating and multiple tags corresponding to each of the attributes; annotating an image category to which the image to be annotated belongs, according to vector similarity between the image tag vector and an category tag vector of each of a plurality of image categories, the category tag vector being generated according to the multiple tags corresponding to each of the attributes.

Abstract: A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.

Abstract: The present disclosure relates to an image attribute classification method, apparatus, electronic device, medium, and program product. The present disclosure enables inputting the image to a feature extraction network to obtain a feature map after feature extraction and N times down-sampling, wherein at least one attribute of the image occupies a second rectangular position area in the feature map after N times down-sampling; calculating a mask function of the at least one attribute of the feature map after N times down-sampling based on the second rectangular position area; obtaining a feature corresponding to the at least one attribute by dot multiplying the feature map after N times down-sampling with the mask function; and inputting the obtained feature corresponding to the at least one attribute to the corresponding attribute classifier for attribute classification.

Abstract: The present disclosure provides a training method and device for an image identifying model, and an image identifying method. The training method comprises: obtaining image samples of a plurality of categories; inputting image samples of each category into a feature extraction layer of the image identifying model to extract a feature vector of each image sample; calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category; establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information; obtaining augmented sample features of the each category based on the augmented distribution function; and inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning.

Abstract: An image processing method includes acquiring a set of image samples for training an attribute recognition model, wherein the set of image samples includes a first subset of image samples with category labels and a second subset of image samples without category labels; training a sample prediction model using the first subset of image samples, and predicting categories of the image samples in the second subset of image samples using the trained sample prediction model; determining a category distribution of the set of image samples based on the category labels of the first subset of image samples and the predicted categories of the second subset of image samples; and acquiring a new image sample if the determined category distribution does not conform to the expected category distribution, and adding the acquired new image sample to the set of image samples.