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.
翻译:完全监督的显要物体探测方法(SOD)取得了巨大进展,但这类方法往往依赖大量像素级说明,这些像素级说明耗时费时费力。在本文中,我们侧重于混合标签下新的监管不力的SOD任务,监管标签包括由传统不受监督的方法和少量真实标签产生的大量粗皮标签。为了解决这一任务中的标签噪音和数量不平衡问题,我们设计了一个具有三项精密培训战略的新的编审流程框架。在模型框架方面,我们把这一任务分包放在标签上精细的子任务和突出目标检测子任务上,这些任务彼此合作,并进行交替培训。具体地说,R-Net设计成一个双流的编码解码模型,配有指导和聚合机制(BGAGA),目的是纠正更可靠的伪标签中的粗皮标签,而S-Net则是一个由当前R-Net所生成的虚拟的高级性标尺所监督的可替换的SOD网络网络网络。我们只需在S-RONet上进行伪标签的升级的升级性测试,S-recialalality train ruder train astration servestration survestration survelate sq survestration surviews