Boosting sparsely annotated shadow detection

Sparsely annotated image segmentation has gained popularity due to its ability to significantly reduce the labeling burden on training data. However, existing methods still struggle to learn complete object structures, especially for complex shadow objects. This paper discusses two prevalent issues existing in previous methods, i.e., generating noisy pseudo labels and misdetecting ambiguous regions. To tackle these challenges, we propose a novel weakly-supervised learning framework to boost sparsely annotated shadow detection. Concretely, a reliable label propagation (RLP) scheme is first designed to diffuse sparse annotations into unlabeled regions, thereby generating denser pseudo shadow masks. This scheme effectively reduces the number of noisy labels by incorporating uncertainty analysis. Then, a multi-cue semantic calibration (MSC) strategy is presented to refine the semantic features extracted from the backbone by employing edge, global, and adjacent priors. Embedded with MSC, the detection network becomes more discriminative against ambiguous regions. By combining RLP and MSC, the proposed weakly-supervised framework can detect complete and accurate shadow regions from sparse annotations. Experimental results on three benchmark datasets demonstrate that our method achieves comparable performance to recent fully-supervised methods, while requiring only about 4.5% of the pixels to be labeled.Graphical abstractBoosting sparsely annotated shadow detection