Semantic Progressive Guidance Network for RGB-D Mirror Segmentation

Abstract

Existing salient target detection methods tend to use a single-mirror segmentation strategy, which ignores feature hierarchy information in the frequency domain and lacks fine-grained correspondence. To address these challenges, we propose a new semantic progressive guidance network (SPGNet). To mine sufficient effective information, we propose the wavelet bidirectional focusing (WBF) module to aggregate sub-band features through a bidirectional wavelet transform and fuse them with low-level features to deepen the detail mining. We also introduce the Gaussian fusion complementary (GFC) module, which adopts Gaussian filtering technology to optimize the feature space and then efficiently extracts the contour information through enhanced feature processing. In addition, we propose a global correlation bootstrapping (GCB) module that constructs region-to-pixel correlations from a global perspective to achieve fine-grained correspondence. The proposed model achieves competitive results on a benchmark dataset.