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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> The performance of salient object detection (SOD) has been significantly advanced by using deep convolutional networks. However, it largely depends on the high cost of pixel-level annotations. To reduce human effort while improving the prediction accuracy, we propose a novel two-phase learning framework. The weakly supervised information in terms of scribbles is provided as initial labels. Then, as the first phase, high-quality pseudo-labels are generated by mapping scribbles onto object\/object-part contours. These contour maps are predicted by the hierarchical contour detection algorithm, providing superior accuracy and smoothness. In the second phase, a deep neural network is alternately trained and predicted. The pseudo-labels are refined in an iterated process, where a conditional random field (CRF) model and a filter module are designed to promote the performance. 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