A cross dual branch guidance network for salient object detection

Yiru Wei, Zhiliang Zhu, Hai Yu, Wei Zhang

Abstract: The effective integration of multi-level contextual information is crucial for deep learning-based salient object detection. However, most existing approaches either adopt the parallel structure or the progressive structure to predict salient objects, which still face challenges in consistently and accurately detecting salient objects of varying scales. In this paper, we propose a novel cross dual branch guidance network to effectively extract the rich semantic features and gradually enhance the saliency map scale-by-scale. Concretely, the parallel branch is guided by the progressive branch to obtain coarse location information of salient objects. In turn, the progressive branch is able to obtain uniform semantics and rich details to enhance saliency map with the guidance of the parallel branch. To obtain the dynamic receptive field, a dynamic sampling module (DSM) is introduced, which can dynamically adjust the sampling positions such that the spatial details of salient objects in complex scenes can be well recognized. In addition, we design a global context module (GCM) to explore the correlation between different parts of salient object or different salient objects, which is favorable for improving the completeness of saliency map. Experiments on five released benchmark datasets demonstrate the effectiveness and superiority of our proposed approach against other state-of-the-art methods.