Morphology-Guided Network via Knowledge Distillation for RGB-D Mirror Segmentation

Mirror segmentation is an emerging computer vision task that is extensively applied in various fields. However, it presents significant challenges to existing segmentation methods when irregular shapes are involved. Most methods are designed for deployment on heavy-duty host machines that demand substantial computational resources and storage capacity, which limits their feasibility for deployment on mobile devices, where efficient and resource-friendly solutions are required. Therefore, we propose a morphology-guided network (MGNet) with knowledge distillation, called MGNet-S*, to achieve the efficiency required for deployment in mobile devices. In this network, we introduce an erosion dilation fusion module that leverages morphological knowledge to extract texture details from intrinsic features. This module incorporates different optimization strategies for multimodal features. Furthermore, it provides a knowledge-distillation framework specifically tailored to the proposed MGNet-S*. The MGNet-S* includes three effective distillation modules: a semi-soft label, misaligned features, and adaptive aggregation types. These modules facilitate the efficient transfer of knowledge from the MGNet teacher to MGNet student, allowing the lightweight network, MGNet-S*, to achieve remarkable performance. Numerous experiments proved that our proposed MGNet-S* outperformed state-of-the-art methods, achieving an 88.6% reduction in parameter count and 82.5% reduction in floating-point operations compared to those of the MGNet teacher network.