A non-negative feedback self-distillation method for salient object detection.

Self-distillation methods utilize Kullback-Leibler divergence (KL) loss to transfer the knowledge from the network itself, which can improve the model performance without increasing computational resources and complexity. However, when applied to salient object detection (SOD), it is difficult to effectively transfer knowledge using KL. In order to improve SOD model performance without increasing computational resources, a non-negative feedback self-distillation method is proposed.

Polarization-driven camouflaged object segmentation via gated fusion.

Recently, polarization-based models for camouflaged object segmentation have attracted research attention. However, to construct this camouflaged object segmentation model, the main challenge is to effectively fuse polarization and light intensity features. Therefore, we propose a multi-modal camouflaged object segmentation method via gated fusion.