PSINet: Progressive Saliency Iteration Network for RGB-D Salient Object Detection

RGB-D Salient Object Detection (RGB-D SOD) is a pixel-level dense prediction task that can highlight the prominent object in the scene by combining color information and depth constraints. Attention mechanisms have been widely employed in SOD due to their ability to capture important cues. However, most existing attentions (\textite.g., spatial attention, channel attention, self-attention) mainly exploit the pixel-level attention maps, ignoring the region properties of salient objects. To remedy this issue, we propose a progressive saliency iteration network (PSINet) with a region-wise saliency attention to improve the regional integrity of salient objects in an iterative manner. Specifically, two-stream Swin Transformers are first employed to extract RGB and depth features. Second, a multi-modality alternate and inverse module (AIM) is designed to extract complementary features from RGB-D images in an interleaved manner, which breaks down the barriers of inconsistency existing in the cross-modal data and also sufficiently captures the complementarity. Third, a triple progressive iteration decoder (TPID) is proposed to optimize the salient objects, where a coarse saliency map, generated by integrating multi-scale features with a U-Net, is viewed as region-wise attention maps to construct a region-wise saliency attention module(RSAM), which can emphasize the prominent region of features. Finally, the regional integrity of salient objects can be gradually optimized from coarse to fine by iterating the above steps on TPID. Quantitative and qualitative experiments demonstrate that the proposed model performs favorably against 19 state-of-the-art (SOTA) saliency detectors on five benchmark RGB-D SOD datasets.