A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

• URL: http://arxiv.org/abs/2209.02957v1
• Date: Wed, 7 Sep 2022 06:45:39 GMT
• Title: A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
• Authors: Runmin Cong, Qi Qin, Chen Zhang, Qiuping Jiang, Shiqi Wang, Yao Zhao, and Sam Kwong
• Abstract summary: This paper focuses on a new weakly-supervised salient object detection (SOD) task under hybrid labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods.
• Score: 96.56299163691979
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. In terms of model framework, we decouple the task into label refinement sub-task and salient object detection sub-task, which cooperate with each other and train alternately. Specifically, the R-Net is designed as a two-stream encoder-decoder model equipped with Blender with Guidance and Aggregation Mechanisms (BGA), aiming to rectify the coarse labels for more reliable pseudo-labels, while the S-Net is a replaceable SOD network supervised by the pseudo labels generated by the current R-Net. Note that, we only need to use the trained S-Net for testing. Moreover, in order to guarantee the effectiveness and efficiency of network training, we design three training strategies, including alternate iteration mechanism, group-wise incremental mechanism, and credibility verification mechanism. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods both qualitatively and quantitatively.

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