Abstract: A method and a device for generating an emoticon are provided. A first expression tag list corresponding to a face image in a portrait is acquired by inputting the face image into an expression recognition model. Additionally, at least one label text corresponding to the face image is determined based on the first expression tag list and a correspondence between a preset text and a second expression tag list. Furthermore, an expression image corresponding to the portrait is determined, where the face image is a part of the expression image. Moreover, an emoticon is generated by labelling the expression image with the at least one label text.

Abstract: An annotation method and device and a storage medium are provided. The annotation method includes operations as follows. A first probability value that a first sample image is annotated with an Nth tag when the first sample image is annotated with an Mth tag is determined based on first tag information of a first image set. M and N are unequal and are positive integers. The first probability value is added to second tag information of a second sample image annotated with the Mth tag in a second image set.

Abstract: Aspects of the disclosure can provide method for identifying a face where multiple images to be identified are received. Each of the multiple images includes a face image part. Each face image of face images in the multiple images to be identified is extracted. An initial figure identification result of identifying a figure in the each face image is determined by matching a face in the each face image respectively to a face in a target image in an image identification library. The face images are grouped. A target figure identification result for each face image in each group is determined according to the initial figure identification result for the each face image in the each group.

Abstract: A method and a device for generating an emoticon are provided. A first expression tag list corresponding to a face image in a portrait is acquired by inputting the face image into an expression recognition model. Additionally, at least one label text corresponding to the face image is determined based on the first expression tag list and a correspondence between a preset text and a second expression tag list. Furthermore, an expression image corresponding to the portrait is determined, where the face image is a part of the expression image. Moreover, an emoticon is generated by labelling the expression image with the at least one label text.

Abstract: Aspects of the disclosure can provide method for identifying a face where multiple images to be identified are received. Each of the multiple images includes a face image part. Each face image of face images in the multiple images to be identified is extracted. An initial figure identification result of identifying a figure in the each face image is determined by matching a face in the each face image respectively to a face in a target image in an image identification library. The face images are grouped. A target figure identification result for each face image in each group is determined according to the initial figure identification result for the each face image in the each group.

Abstract: An image processing method includes: inputting an original image and a style reference image into a convolutional neural network to generate a first feature map of the original image and a second feature map of the style reference image; calculating a correction value of any pixel point in the first feature map based on the second feature map; replacing an original value of each pixel point in the first feature map with a corresponding corrected value to obtain a third feature map; and performing a deconvolution process on the third feature map to obtain a processed image. An apparatus and a storage medium are further provided.

Abstract: An annotation method and device and a storage medium are provided. The annotation method includes operations as follows. A first probability value that a first sample image is annotated with an Nth tag when the first sample image is annotated with an Mth tag is determined based on first tag information of a first image set. M and N are unequal and are positive integers. The first probability value is added to second tag information of a second sample image annotated with the Mth tag in a second image set.

Abstract: An image processing method includes: inputting an original image and a style reference image into a convolutional neural network to generate a first feature map of the original image and a second feature map of the style reference image; calculating a correction value of any pixel point in the first feature map based on the second feature map; replacing an original value of each pixel point in the first feature map with a corresponding corrected value to obtain a third feature map; and performing a deconvolution process on the third feature map to obtain a processed image. An apparatus and a storage medium are further provided.