Weakly supervised semantic segmentation method and device based on a commonality-specificity supervision mechanism

Abstract

The present invention discloses a weakly supervised semantic segmentation method and device based on a commonality-specificity supervision mechanism, the contrastive convolution module is established to identify ambiguous boundary regions within the image based on the convolutional cognitive differences of different receptive fields within the image, overcoming the problem of blurred segmentation boundaries in weakly supervised semantic segmentation tasks; the commonality-specificity supervision module is established, using the commonality supervision mechanism to discover similar structural background distributions between different classes of images, the specificity supervision mechanism is used to identify prominent regions in the image distribution and achieve semantic segmentation of the target object, this not only improves the sparsity of the localization region, but also optimized the segmentation boundary; the knowledge gap module constructs the contrastive generated images with enhanced structural distribution, the knowledge gap between the contrastive generated images and the class images effectively overcomes the incomplete activation correspondence in mainstream methods and improves the weakly supervised semantic segmentation performance at the image level.