Domain adaptive semantic segmentation method based on similarity space alignment

Abstract

The invention discloses a domain adaptive semantic segmentation method based on similarity space alignment. The segmentation output of a source domain and the segmentation output of a target domain are respectively transformed into a similarity space, and the similarity space distribution of the source domain and the target domain is aligned to reduce the inter-domain difference, so that a semantic segmentation model with a better segmentation effect on an unsupervised target domain can be obtained. According to the method, the concept of similarity space is introduced into a cross-domain semantic segmentation task, the correlation between categories in a segmentation scene is better coded, and the discriminator is used for discriminating the similarity space of different domains, so thatthe segmentation network pays more attention to the structure, category coexistence and other information of an image, and the whole network can be trained end to end. The unsupervised domain self-adaptive semantic segmentation method based on similarity space alignment is innovated on the basis of the existing technical thought, the correlation space information of categories in a segmentation scene is fused, the segmentation performance is better, and the method has very high practical application value.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and more specifically relates to a domain adaptive semantic segmentation method based on similarity space alignment. Background technique [0002] In the field of computer vision, semantic segmentation is one of the most fundamental and important tasks. Semantic segmentation is the technical basis of many high-tech applications such as autonomous driving, robot navigation, and smart medical care. With the widespread application of convolutional neural networks in computer vision, various new semantic segmentation networks have made great progress in semantic segmentation tasks under strong supervision in recent years. However, the current strongly supervised segmentation network requires a large amount of labeled data, and the densely labeled semantic segmentation dataset requires a lot of time and manpower. In order to solve the segmentation performance bottleneck caused by labeling se...

AI Technical Summary

Problems solved by technology

However, it is still challenging to obtain semantic segmentation models with high segmentation performance on the target domain

Due to the strong category correlation and coexistence among semantic segmentation result categories, for example, the category "sky" always appears above the category "building", and the category "cyclist" is always accompanied by "bicycle" or "motorcycle". Category, real images and synthetic images are consistent in category correlation and category coexistence, current methods ignore this feature

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