Exploring multi-scale deformable context and channel-wise attention for salient object detection

Contextual information has played an important role in salient object detection. However, due to the fixed geometric structures of convolution kernels employed by existing Convolutional Neural Networks (CNNs) based methods, it is difficult to extract meaningfully visual contexts for those salient objects with varying sizes and non-rigid shapes. To address this problem, in this paper, we propose a Multi-Scale Deformation Module (MSDM) to capture multi-scale visual cues and varying shapes of salient objects. Moreover, most existing CNNs based methods treat all channels of feature maps equally, which tends to differ from the fact that different channels actually contribute differently to saliency prediction. For that, we involve a novel Channel-Wise Attention Mechanism (CWAM) after MSDM to highlight those informative channels while suppressing those confusing ones. Experimental results on five benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a new Multi-Scale Deformation Module (MSDM) and Channel-Wise Attention Mechanism (CWAM) to extract salient objects of varying sizes and shapes, while highlighting informative channels and suppressing confusing channels. Experimental results demonstrate the superiority of the proposed method over state-of-the-art approaches.