LDDG: Long-distance dependent and dual-stream guided feature fusion network for co-saliency object detection

Abstract

Complex image scenes are a challenge in the collaborative saliency object detection task in the field of saliency detection, such as the inability to accurately locate salient object, surrounding background information affecting object recognition, and the inability to fuse multi-layer collaborative features well. To solve these problems, we propose a long-range dependent and dual-stream guided feature fusion network. Firstly, we enhance saliency feature by the proposed coordinate attention module so that the network can learn a better feature representation. Secondly, we capture the long-range dependency information of image feature by the proposed non-local module, to obtain more comprehensive contextual complex information. At lastly, we propose a dual-stream guided network to fuse multiple layers of synergistic saliency features. The dual-stream guided network includes classification streams and mask streams, and the layers in the decoding network are guided to fuse the feature of each layer to output more accurate synoptic saliency prediction map. The experimental results show that our method is superior to the existing methods on three common datasets: CoSal2015, CoSOD3k, and CoCA.