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By the aid of attention mechanisms to weight the image features adaptively,recent advanced deep learning-based models encourage the predicted results toapproximate the ground-truth masks with as large predictable areas as possible,thus achieving the state-of-the-art performance. However, these methods do notpay enough attention to small areas prone to misprediction. In this way, it isstill tough to accurately locate salient objects due to the existence ofregions with indistinguishable foreground and background and regions withcomplex or fine structures. To address these problems, we propose a novelconvolutional neural network with purificatory mechanism and structuralsimilarity loss. Specifically, in order to better locate preliminary salientobjects, we first introduce the promotion attention, which is based on spatialand channel attention mechanisms to promote attention to salient regions.Subsequently, for the purpose of restoring the indistinguishable regions thatcan be regarded as error-prone regions of one model, we propose therectification attention, which is learned from the areas of wrong predictionand guide the network to focus on error-prone regions thus rectifying errors.Through these two attentions, we use the Purificatory Mechanism to imposestrict weights with different regions of the whole salient objects and purifyresults from hard-to-distinguish regions, thus accurately predicting thelocations and details of salient objects. In addition to paying differentattention to these hard-to-distinguish regions, we also consider the structuralconstraints on complex regions and propose the Structural Similarity Loss. Inexperiments, the proposed approach outperforms 19 state-of-the-art methods onsix datasets with a notable margin at over 27FPS on a single NVIDIA 1080Ti GPU.

Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss