Few-shot semantic segmentation based on multiscale 4D feature fusion
Abatract The existing semantic segmentation method relies on sufficient pixel-level image labeling,while the segmentation model needs to be trained under the new sample conditions,which brings the problem of manually labeling images.Therefore,few-shot semantic segmentation method is proposed to solve such problems.The current few-shot segmentation method mainly adopts the prototype learning method,while the prototype learning method lacks pixel-level support-level features to guide query image segmentation,resulting in the problem of low segmentation accuracy.Based on this,a four-dimensional feature fusion and attention-enhancing few-shot segmentation network is designed.In order to obtain the pixel-level representation information of rich support set features for query images,four-dimensional convolution is used in the feature pyramid structure to gradually compress advanced semantic features and intermediate semantic features into super-correlated features,which is then used to segment the query image.At the same time,the test results of mIoU in the PASCAL-5i dataset 1-shot setting were improved by 0.6%and 2.3%compared to the HSNet and PFENet,respectively.
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