Saliency Prototype for RGB-D and RGB-T Salient Object Detection

Zihao Zhang, Jie Wang, Yahong Han

Abstract: Most of the existing bi-modal (RGB-D or RGB-T) salient object detection methods attempt to integrate multimodality information through various fusion strategies. However, existing methods lack a clear definition of salient regions before feature fusion, which results in poor model robustness. To tackle this problem, we propose a novel prototype, the saliency prototype, which captures common characteristic information among salient objects. A prototype contains inherent characteristics information of multiple salient objects, which can be used for feature enhancement of various salient objects. By utilizing the saliency prototype, we provide a clearer definition of salient regions and enable the model to focus on these regions before feature fusion, avoiding the influence of complex backgrounds during the feature fusion stage. Additionally, we utilize the saliency prototypes to address the quality issue of auxiliary modality. Firstly, we apply the saliency prototypes obtained by the primary modality to perform semantic enhancement of the auxiliary modality. Secondly, we dynamically allocate weights for the auxiliary modality during the feature fusion stage in proportion to its quality. Thus, we develop a new bi-modal salient detection architecture Saliency Prototype Network (SPNet), which can be used for both RGB-D and RGB-T SOD. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate the effectiveness of the proposed approach against the state-of-the-art. Our code is available at https://github.com/ZZ2490/SPNet.