SACNet: Saliency-Aided Aggregation Consensus Network for RGB-D Co-Salient Object Detection
Zhangping Tu, Xiaohong Qian, Wujie Zhou · IEEE Signal Processing Letters · 2025
Red, green, and blue-depth (RGB-D) co-salient object detection identifies and segments co-appearing salient targets in various related images and corresponding depth maps. Existing methods directly blend depth features with RGB features. However, they often overlook that, in depth maps, the low contrast of neighboring objects affects salient regions that do not correspond to the saliency regions in the RGB image, leading to unsatisfactory results. We design a novel network called saliency-aided aggregation consensus network (SACNet) for RGB-D co-salient object detection. SACNet incorporates two key modules: the multimodal weight-sharing fusion and feature consensus aggregation module. The multimodal weight-sharing fusion effectively integrates RGB and depth modal information by calibrating depth features and sharing weights, which helps extract feature consensus. The feature consensus aggregation module clusters information in individual image saliency for inference, distinguishing between co-salient and non-co-salient objects, and aggregates consensus features across the image group. To enhance the discriminative power of the consensus representation, individual image saliency information is stored and updated in a queue. SACNet achieved competitive results on two challenging benchmark datasets.