Domain adaptive image semantic segmentation method based on GAN

A semantic segmentation and image technology, applied in the field of GAN-based domain-adaptive image semantic segmentation, can solve the problem of low segmentation accuracy, and achieve the effect of improving accuracy and segmentation accuracy.
CN110533044AActive Publication Date: 2019-12-03GUANGDONG UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGDONG UNIV OF TECH
Publication Date
2019-12-03

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Abstract

The invention relates to the technical field of image semantic segmentation, in particular to a domain adaptive image semantic segmentation method based on GAN. The method comprises a semantic segmentation network, a generative adversarial module, a spatial receptive field module and an adversarial module group. The target domain is a label-free data set, the source domain is a label data set, andthe task is to obtain a semantic segmentation label graph of the target domain. The inside of a classical image semantic segmentation network can be regarded as an encoder and a decoder, and an inputimage is encoded and decoded to obtain an output image with the same size. Corresponding adversarial training auxiliary modules are added to an encoder and a decoder respectively to reduce the domaindrift problem caused by domain adaptation. According to the invention, the problem of low accuracy of unsupervised image semantic segmentation in the prior art is solved.
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Description

technical field

[0001] The invention relates to the technical field of image semantic segmentation, in particular to a GAN-based domain-adaptive image semantic segmentation method. Background technique

[0002] At present, the deep neural network is very capable of learning a very good visual model under the premise of big data. But collecting label data, even pixel-level label data, is very difficult. According to reports, it takes at least 90 minutes to manually label a picture with pixel-level labels. For unlabeled semantic segmentation of image datasets, that is, unsupervised semantic segmentation, one of the more popular options in recent years is to use virtual data (such as images in game scenes, we can easily obtain pictures and marked pixel-level labels, several orders of magnitude faster than manual labeling) to generate image labels for real-world scenes. However, simply applying the model trained on the virtual data to the actual picture results in a poor pictu...

Claims

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