Self-Knowledge Distillation-Based Staged Extraction and Multiview Collection Network for RGB-D Mirror Segmentation

Integrating RGB and depth information could improve mirror segmentation performance. Therefore, it is important to extract and use both types of information. Previous algorithms have often used existing backbone networks for feature extraction, frequently ignoring the differences between different feature layers. To address this issue, we propose an algorithm for staged feature extraction using a multitype backbone network that combines the features of convolutional neural networks and transformers. In addition, we developed a multiview collector to extract cross-modal fusion features from various perspectives. Furthermore, we applied the self-knowledge distillation technique to the proposed algorithm, thereby improving the model's performance. The proposed model achieved competitive results on a benchmark dataset.