Scribble-Supervised Salient Object Detection with Supervised Contrastive Consistency Regularization: A Comprehensive Study

Salient object detection (SOD) is a fundamental computer vision task that aims to identify and segment the most visually attractive regions in natural images. While fully supervised methods achieve high accuracy, they rely on costly pixel-wise labels. Scribble annotation offers an efficient alternative with sparse foreground and background strokes, yet it introduces challenges including supervision sparsity, background interference, and boundary ambiguity. This paper studies the SCWSSOD framework for scribble-supervised SOD and proposes a supervised contrastive consistency (SCC) loss to address its limitations. Extensive experiments across six benchmark datasets (PASCAL-S, DUTS-TE, ECSSD, DUT-OMRON, HKU-IS, THUR15K) demonstrate consistent improvements over the baseline. Notably, on the challenging PASCAL-S dataset, our method increases the maximum F-measure from 0.863 to 0.871 and reduces the mean absolute error from 0.122 to 0.113. Both quantitative metrics and qualitative visualizations indicate the effectiveness of our approach in resolving boundary ambiguity and suppressing background noise, suggesting a promising solution for weakly-supervised SOD.