  Despite the powerful feature extraction capability of Convolutional Neural
Networks, there are still some challenges in saliency detection. In this paper,
we focus on two aspects of challenges: i) Since salient objects appear in
various sizes, using single-scale convolution would not capture the right size.
Moreover, using multi-scale convolutions without considering their importance
may confuse the model. ii) Employing multi-level features helps the model use
both local and global context. However, treating all features equally results
in information redundancy. Therefore, there needs to be a mechanism to
intelligently select which features in different levels are useful. To address
the first challenge, we propose a Multi-scale Attention Guided Module. This
module not only extracts multi-scale features effectively but also gives more
attention to more discriminative feature maps corresponding to the scale of the
salient object. To address the second challenge, we propose an Attention-based
Multi-level Integrator Module to give the model the ability to assign different
weights to multi-level feature maps. Furthermore, our Sharpening Loss function
guides our network to output saliency maps with higher certainty and less
blurry salient objects, and it has far better performance than the
Cross-entropy loss. For the first time, we adopt four different backbones to
show the generalization of our method. Experiments on five challenging datasets
prove that our method achieves the state-of-the-art performance. Our approach
is fast as well and can run at a real-time speed.
