Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
This work addresses a domain-specific problem in computer vision for improving salient object detection using RGB-D data, with incremental contributions.
The paper tackles the problem of cross-modal fusion and multi-scale refinement in RGB-D salient object detection by introducing a top-down and bottom-up iterative refinement architecture and an attention-based fusion module, achieving effectiveness as shown in experiments on seven public datasets.
The extensive research leveraging RGB-D information has been exploited in salient object detection. However, salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Meanwhile, similar salient patterns are available in cross-modal depth images as well as multi-scale versions. Cross-modal fusion and multi-scale refinement are still an open problem in RGB-D salient object detection task. In this paper, we begin by introducing top-down and bottom-up iterative refinement architecture to leverage multi-scale features, and then devise attention based fusion module (ABF) to address on cross-modal correlation. We conduct extensive experiments on seven public datasets. The experimental results show the effectiveness of our devised method