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...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2024-11, Vol.54 (21), p.10541-10560
Main Authors: Zhou, Kai, Shao, Yanli, Fang, Jinglong, Wei, Dan, Sun, Wanlu
Format: Article
Language:English
Subjects:
Annotations
Artificial Intelligence
Computer Science
Feature extraction
Image annotation
Image segmentation
Labels
Machine learning
Machines
Manufacturing
Mechanical Engineering
Processes
Semantics
Shadow masks
Shadows
Supervised learning
Uncertainty analysis
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Summary: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 abstract Boosting sparsely annotated shadow detection
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05740-3