RCNet: Related Context-Driven Network with Hierarchical Attention for Salient Object Detection

Abstract

Recent progress in salient object detection (SOD) mainly depends on dilated convolution with different receptive fields to capture contextual information for multi-scale learning. Intuitively, contextual information in different scales is conducive to understanding the image content, and thus can help us identify and locate salient objects in real-world scenes. However, the sparsity inside the dilated convolution kernel may cause the problem of local information loss, limiting the predictive accuracy of the model. In addition, the inequality of feature channels should also be considered, and they often feature different deviations for salient objects or background noises. Although some channel attention mechanisms have been proposed in SOD, their ability to capture global information is limited, and the problem of high complexity is still a great challenge. To alleviate the abovementioned problems, we propose a Related Context-Driven Network (RCNet) with Hierarchical Attention for Salient Object Detection, consisting of a cascaded multi-scale context exploration (CMCE) module and a hierarchical feature aggregation (HFA) module. The CMCE module is to capture multi-scale contextual information through using multi-receptive-field dilated convolutions in a diamond hierarchical structure, where a feature reconstruction operation is deployed to improve the correlation of features, effectively avoiding the gridding problems and local information loss. Meanwhile, the HFA module adaptively interacts with the complementary information of the multi-level features to further capture the important information from within the feature channel by a multi-source hybrid channel attention (MHCA) mechanism to generate powerful and robust feature representations. Extensive experiments on six benchmark datasets demonstrate that the proposed RCNet method consistently outperforms 20 existing the state-of-the-art SOD methods in terms of accuracy, generalization capacity and robustness.