A complementary dual model for weakly supervised salient object detection

Abstract

Leveraging scribble annotations for weakly supervised salient object detection significantly reduces reliance on extensive, precise labels during model training. To optimize the use of these sparse annotations, we introduce a novel framework called the Complementary Reliable Region Aggregation Network (CRANet). This framework utilizes a dual-model framework that integrates complementary information from two models with the same architecture but different parameters: a foreground model that generates the saliency map and a background model that identifies regions excluding salient objects. By merging the outputs of both models, we propose a reliable pseudo-label aggregation strategy that expands the supervision capability of scribble annotations, eliminating the necessity for predefined thresholds and other parameterized modules. High-confidence predictions are then combined to create pseudo labels that guide the training process of both models. Additionally, we incorporate a flipping consistency method and a flipped guided loss function to enhance prediction consistency and increase the scale of the training set, effectively addressing the challenges posed by sparse and structurally constrained scribble annotations. Experimental results demonstrate that our approach significantly outperforms existing methods.