Dual attention guided multi-scale fusion network for RGB-D salient object detection

While recent research on salient object detection (SOD) has shown remarkable progress in leveraging both RGB and depth data, it is still worth exploring how to use the inherent relationship between the two to extract and fuse features more effectively, and further make more accurate predictions. In this paper, we consider combining the attention mechanism with the characteristics of the SOD, proposing the Dual Attention Guided Multi-scale Fusion Network. We design the multi-scale fusion block by combining multi-scale branches with channel attention to achieve better fusion of RGB and depth information. Using the characteristic of the SOD, the dual attention module is proposed to make the network pay more attention to the currently unpredicted saliency regions and the wrong parts in the already predicted regions. We perform an ablation study to verify the effectiveness of each component. Quantitative and qualitative experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance.