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Current state-of-the-art saliency detection models rely heavily on largedatasets of accurate pixel-wise annotations, but manually labeling pixels istime-consuming and labor-intensive. There are some weakly supervised methodsdeveloped for alleviating the problem, such as image label, bounding box label,and scribble label, while point label still has not been explored in thisfield. In this paper, we propose a novel weakly-supervised salient objectdetection method using point supervision. To infer the saliency map, we firstdesign an adaptive masked flood filling algorithm to generate pseudo labels.Then we develop a transformer-based point-supervised saliency detection modelto produce the first round of saliency maps. However, due to the sparseness ofthe label, the weakly supervised model tends to degenerate into a generalforeground detection model. To address this issue, we propose a Non-SalientSuppression (NSS) method to optimize the erroneous saliency maps generated inthe first round and leverage them for the second round of training. Moreover,we build a new point-supervised dataset (P-DUTS) by relabeling the DUTSdataset. In P-DUTS, there is only one labeled point for each salient object.Comprehensive experiments on five largest benchmark datasets demonstrate ourmethod outperforms the previous state-of-the-art methods trained with thestronger supervision and even surpass several fully supervised state-of-the-artmodels. The code is available at: https://github.com/shuyonggao/PSOD.

Weakly-Supervised Salient Object Detection Using Point Supervision