Unsupervised salient object detection with pseudo-labels refinement.

Abstract

In Salient Object Detection (SOD), most methods rely on manually annotated labels, which are costly. As a result, unsupervised methods have gained significant attention. Existing methods often generate noisy pseudo-labels using traditional techniques, which can affect model performance. To address this, we propose an unsupervised method for RGB image salient object detection that generates high-quality pseudo-labels without manual annotation and uses them to train the detection model. The method generates initial pseudo-labels and improves their quality by introducing contrastive learning pre-trained weights and a pseudo-label self-updating strategy. Additionally, we design a detection network with a Multi-Feature Aggregation (MFA) module and a Context Feature Interaction (CFI) module to enhance the model’s ability to detect salient objects in complex scenarios. The model we proposed, trained with our pseudo-labels, shows significant improvement on USOD and achieves excellent scores on public benchmarks.