Image editing algorithm for guiding object representation splitting based on single sample

Abstract

The invention discloses an image editing algorithm for guiding object representation splitting based on a single sample. The image editing algorithm comprises the following steps: 1) constructing a supervision module based on the single sample; 2) constructing a dual strategy in a self-supervision module under the guidance of a single sample; 3) constructing a fuzzy classification strategy in a self-supervision module under the guidance of a single sample; and 4) guiding image editing of object representation splitting based on a single sample annotation image. According to the image editing algorithm based on single-sample guided object representation splitting established by the steps, only one sample needs to be marked for each category of images to form a single-sample sample, and a large amount of unmarked data training is guided by utilizing the single-sample sample; foreground object and background representation splitting of a complex scene is achieved through a single-sample supervision module and a single-sample guided label-free data self-supervision module, so that an image can be directly operated in an image representation space, and a related image editing task is easily achieved.

Description

technical field [0001] The invention belongs to the field of image representation learning (splitting), and can map the foreground object and background in the image to a representation with modular physical meaning. This representation can also be split into foreground object information and background information, which can be used in The high-dimensional representation space of images directly operates on foreground objects and backgrounds, and can be flexibly used in applications such as image editing. Aiming at the problems that supervised image representation splitting based on deep learning requires a large amount of labeled data and existing unsupervised methods are ineffective on complex background images, an image editing algorithm based on single-sample guided object representation splitting is proposed. Each Class image only uses one labeled sample (single sample) to guide all other unlabeled samples to learn, so that for a large amount of unlabeled data, a very sm...

AI Technical Summary

Problems solved by technology

[0006] The present invention aims to solve the problem that the current image object representation split based on deep learning requires a large number of labels and can only be applied to simple data, and provides an image editing algorithm based on single-sample guided object representation split

[0007] At present, image editing, such as exchanging the foreground objects of two images, can only be carried out in the image, which often requires Photoshop or the use of foreground and background segmentation models. If the size, position, and shape of the objects in the images are inconsistent, pixel black holes will appear. The problem of repairing, the current representation decoupling learning work can only realize the decoupling of attributes, such as color, size, etc., and it is often uncontrollable. Even if it is transferred to object representation splitting, a large number of labeled samples are required

In order to solve the problem that the deep network requires a large number of annotations and can only be applied to simple data, the present invention designs an image editing algorithm based on single-sample guided object representation splitting

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