Cross-modal collaborative propagation for RGB-T saliency detection

Recently, RGB-T saliency detection becomes gradually a hot topic due to the fact that RGB-T multi-modal data could overcome the limitation of conventional RGB data in some cases. However, existing RGB-T saliency detection methods usually fail to take both advantages of two modalities and cannot boost performance effectively. Therefore, we achieve RGB-T saliency detection via a novel method, namely cross-modal collaborative propagation (CMCP), which contains a novel saliency propagation mechanism and a novel cross-modal collaborative learning framework relied on the proposed propagation mechanism. More specifically, we firstly propose a novel saliency propagation method and then, respectively, regard two modalities as inputs to generate RGB-induced and thermal-induced propagation mechanisms. To bridge RGB-T modalities, a novel cross-modal collaborative learning framework between RGB-induced and thermal-induced propagation mechanisms is devised to optimize, respectively, two propagation results. In other words, two modalities constantly extract supervision information to help the opposite side to refine propagation result until attaining a stable state. Finally, we integrate two modalities-induced propagation results into a refined saliency map. We compare our model with the state-of-the-art RGB-T and RGB saliency detection algorithms on three benchmark datasets, and experimental results show that the proposed CMCP achieves the significant improvement.