Boundary-Aware Salient Object Detection via Recurrent Two-Stream Guided Refinement Network
This work addresses a specific challenge in computer vision for applications like image segmentation, but it is incremental as it builds on existing deep learning methods by improving feature integration.
The paper tackles the problem of salient object detection in low-contrast scenes by proposing a Recurrent Two-Stream Guided Refinement Network (RTGRNet) that iteratively refines saliency and boundary features, achieving state-of-the-art performance on six public datasets.
Recent deep learning based salient object detection methods which utilize both saliency and boundary features have achieved remarkable performance. However, most of them ignore the complementarity between saliency features and boundary features, thus get worse predictions in scenes with low contrast between foreground and background. To address this issue, we propose a novel Recurrent Two-Stream Guided Refinement Network (RTGRNet) that consists of iterating Two-Stream Guided Refinement Modules (TGRMs). TGRM consists of a Guide Block and two feature streams: saliency and boundary, the Guide Block utilizes the refined features after previous TGRM to further improve the performance of two feature streams in current TGRM. Meanwhile, the low-level integrated features are also utilized as a reference to get better details. Finally, we progressively refine these features by recurrently stacking more TGRMs. Extensive experiments on six public datasets show that our proposed RTGRNet achieves the state-of-the-art performance in salient object detection.