Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN), which recurrently filters redundant information and explicitly models boundary information for saliency detection. Different from existing refinement methods, we propose a Region Refinement Module (RRM) that optimizes salient region prediction by incorporating supervised attention masks in the intermediate refinement stages. The module only brings a minor increase in model size and yet significantly reduces false predictions from the background. To further refine boundary areas, we propose a Boundary Refinement Loss (BRL) that adds extra supervision for better distinguishing foreground from background. BRL is parameter free and easy to train. We further observe that BRL helps retain the integrity in prediction by refining the boundary. Extensive experiments on saliency detection datasets show that our refinement module and loss bring significant improvement to the baseline and can be easily applied to different frameworks. We also demonstrate that our proposed model generalizes well to portrait segmentation and shadow detection tasks.
翻译:尽管经过仔细研究,但虚假预测和不明确的边界仍然是突出物体探测的主要问题。在本文件中,我们提议建立一个区域精炼网(区域精炼网),它经常过滤多余的信息,并明确为显著探测提供边界信息的模型。与现有的精细方法不同,我们提议建立一个区域精炼模(区域精炼模),通过在中期精炼阶段纳入受监督的注意面罩,优化突出区域的预测。该模件只带来微小的模型尺寸,并大大减少背景上的虚假预测。为了进一步改进边界地区,我们提议建立一个边界精炼网(边界精炼网),增加额外的监督,以更好地区分地面和背景。BRL是免费且易于培训的参数。我们还注意到,BRL有助于通过改进边界来保持预测的完整性。关于突出的探测数据集的广泛实验表明,我们的精炼模模和损失使基线得到显著改进,并且很容易应用于不同的框架。我们还表明,我们提议的模范的精炼模包罗了对分形和影子探测任务进行概括。