Region Refinement Network for Salient Object Detection
This work addresses a persistent issue in salient object detection for computer vision applications, but it is incremental as it builds on existing refinement methods.
The paper tackles false predictions and unclear boundaries in salient object detection by proposing a Region Refinement Network (RRN) with a Region Refinement Module (RRM) and Boundary Refinement Loss (BRL), which significantly reduces false predictions and improves boundary refinement, as shown in extensive experiments on saliency detection datasets.
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.