Salient Object Detection by Contextual Refinement

Sayanti Bardhan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 356-357

Context plays an important role in the saliency prediction task. In this work, we propose a saliency detection framework that not only extracts visual features but also models two kinds of context including object-object relationships within a single image and scene contextual information. Specifically, we develop a novel saliency detection framework with a Contextual Refinement Module (CRM) which consists of two sub-networks, Object Relation Unit (ORU) and Scene Context Unit (SCU). ORU encodes the object-object relationship based on object relative position and object co-occurrence pattern in an image, by graphical approach, while SCU incorporates the scene contextual information of an image. Object Relation Unit (ORU) and Scene Context Unit (SCU) captures complementary contextual information to give a holistic estimation of salient regions. Extensive experiments show the effectiveness of modelling object relations and scene context in boosting the performance of saliency prediction. In particular, our frame-work outperforms the state-of-the-art models on challenging benchmark datasets.