Attention-aware Cross-modal Cross-level Fusion Network for RGB-D Salient Object Detection

Convolutional neural networks have achieved wide success in RGB saliency detection. Recently, the advent of RGB-D sensors such as Kinect provide additional geometric saliency cues. However, the key challenge for RGB-D salient object detection that how to fuse RGB and depth information sufficiently is still under-studied. Traditional works mainly follow the two-stream architecture and combine RGB and depth features/decisions in an early or late point. The multi-modal fusion stage is performed by directly concatenating the features from two modalities without selection. In this work, we address this question by proposing a novel network with a distinguished insight: A selection module is significantly helpful for more informative and sufficient cross-modal cross-level combination. To this end, we introduce a top-down RGB-D fusion network which integrates an attention-aware cross-modal cross-level fusion block in each level to select discriminative features from each level and each modality. Extensive experiments on public datasets show that the proposed network is able to solve the key problems in RGB-D fusion and achieves state-of-the-art performance on RGB-D salient object detection.