Saliency prediction on RGB-D images is an underexplored and challenging task in computer vision. We propose a channel-wise attention and contextual interaction asymmetric network for RGB-D saliency prediction. In the proposed network, a common feature extractor provides cross-modal complementarity between the RGB image and corresponding depth map. In addition, we introduce a four-stream feature-interaction module that fully leverages multiscale and cross-modal features for extracting contextual information. Moreover, we propose a channel-wise attention module to highlight the feature representation of salient regions. Finally, we refine coarse maps through a corresponding refinement block. Experimental results show that the proposed network achieves a performance comparable with state-of-the-art saliency prediction methods on two representative datasets.

在计算机视觉中，对RGB-D图像进行显着性预测是一项尚未开发且具有挑战性的任务。我们提出了针对RGB-D显着性预测的逐通道注意和上下文交互不对称网络。在所提出的网络中，公共特征提取器在RGB图像和相应的深度图之间提供了交叉模式的互补性。此外，我们引入了一个四流功能交互模块，该模块充分利用多尺度和跨模式功能来提取上下文信息。此外，我们提出了一个通道注意模块来突出显示显着区域的特征。最后，我们通过相应的细化块细化粗图。