Supervised contrastive learning with multi-scale interaction and integrity learning for salient object detection

Abstract

Salient object detection (SOD) is designed to mimic human visual mechanisms to identify and segment the most salient part of an image. Although related works have achieved great progress in SOD, they are limited when it comes to interferences of non-salient objects, finely shaped objects and co-salient objects. To improve the effectiveness and capability of SOD, we propose a supervised contrastive learning network with multi-scale interaction and integrity learning named SCLNet. It adopts contrastive learning (CL), multi-reception field confusion (MRFC) and context enhancement (CE) mechanisms. Using this method, the input image is first divided into two branches after two different data augmentations. Unlike existing models, which focus more on boundary guidance, we add a random position mask on one branch to break the continuous of objects. Through the CL module, we obtain more semantic information than appearance information by learning the invariance of different data augmentations. The MRFC module is then designed to learn the internal connections and common influences of various reception field features layer by layer. Next, the obtained features are learned through the CE module for the integrity and continuity of salient objects. Finally, comprehensive evaluations on five challenging benchmark datasets show that SCLNet achieves superior results. Code is available at https://github.com/YuPangpangpang/SCLNet.