Channel-wise Contribution Assessment for RGB-D Salient Object Detection

In RGB-D salient object detection (SOD), a common approach to improving accuracy is by using a dual-stream architecture to combine RGB and depth data. However, the effectiveness of depth information varies depending on the scene. In scenarios where depth maps provide a limited contribution, integrating them with RGB can be challenging and sometimes even detrimental to performance rather than enhancing it. Conventional RGB-D SOD methods often lack precision in assessing depth map quality, neglecting to account for the distinct contributions of various regions within the map, often resulting in a suboptimal fusion of regions where depth information is minimally beneficial or irrelevant. To address these issues, this paper presents a novel channel-wise contribution assessment method that precisely evaluates the contributions of both the RGB and depth channels. By employing a controlled perturbation process to challenge the saliency detection model with specific, manageable disturbances, we are able to measure how much RGB and depth information each contributes to the final saliency map. Based on this analysis, we have developed a novel routing-style fusion of modality that dynamically adjusts the integration of the two modalities. This approach significantly lessens the negative impact of regions where depth data have a low, no, or even detrimental contribution, leading to a more effective and balanced fusion of RGB and depth information. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method consistently achieves competitive performance and improves the robustness of RGB-D salient object detection across diverse and challenging scenarios.