Abstract: To address the challenge that existing multimodal salient object detection methods are easily disrupted by low-quality auxiliary modalities,leading to poor model robustness,this paper proposes a multimodal salient object detection method based on saliency primitive dynamic weighting.Specifically,the process captures the common features of different salient objects and clusters them to obtain saliency primitives,thereby achieving clear definition of salient regions.These saliency primitives are then utilized to dynamically adjust the weighting of different modalities during the fusion stage,ensuring that semantic information from high-quality modalities is fully leveraged while effectively suppressing potential interference from low-quality auxiliary modalities.In addition,a primitive-guided feature alignment mechanism is introduced to effectively reduce the semantic gap between the primary and auxiliary modalities,further enhancing the model's detection performance.This mechanism also enables the model to more accurately capture cross-modal common features,thereby improving the accuracy and stability of detection results.To validate the effectiveness of the proposed method,extensive qualitative and quantitative evaluations are conducted on six RGB-D datasets and three RGB-T datasets.Experimental results demonstrate that the proposed method exhibits strong stability and robustness even in the presence of low-quality auxiliary modalities.

Key words: Multimodal salient object detection, Saliency primitive, Fusion weight, Semantic gap, Feature alignment