Weakly supervised salient object detection via bounding-box annotation and SAM model

Salient object detection (SOD) aims to detect the most attractive region in an image. Fully supervised SOD based on deep learning usually needs a large amount of data with human annotation. Researchers have gradually focused on the SOD task using weakly supervised annotation such as category, scribble, and bounding-box, while these existing weakly supervised methods achieve limited performance and demonstrate a huge performance gap with fully supervised methods. In this work, we proposed one novel two-stage weakly supervised method based on bounding-box annotation and the recent large visual model Segment Anything (SAM). In the first stage, we regarded the bounding-box annotation as the box prompt of SAM to generate initial labels and proposed object completeness check and object inversion check to exclude low quality labels, then we selected reliable pseudo labels for the training initial SOD model. In the second stage, we used the initial SOD model to predict the saliency map of excluded images and adopted SAM with the everything mode to generate segmentation candidates, then we fused the saliency map and segmentation candidates to predict pseudo labels. Finally we used all reliable pseudo labels generated in the two stages to train one refined SOD model. We also designed a simple but effective SOD model, which can capture rich global context information. Performance evaluation on four public datasets showed that the proposed method significantly outperforms other weakly supervised methods and also achieves comparable performance with fully supervised methods.