  Fully convolutional neural networks (FCNs) have shown outstanding performance
in many dense labeling problems. One key pillar of these successes is mining
relevant information from features in convolutional layers. However, how to
better aggregate multi-level convolutional feature maps for salient object
detection is underexplored. In this work, we present Amulet, a generic
aggregating multi-level convolutional feature framework for salient object
detection. Our framework first integrates multi-level feature maps into
multiple resolutions, which simultaneously incorporate coarse semantics and
fine details. Then it adaptively learns to combine these feature maps at each
resolution and predict saliency maps with the combined features. Finally, the
predicted results are efficiently fused to generate the final saliency map. In
addition, to achieve accurate boundary inference and semantic enhancement,
edge-aware feature maps in low-level layers and the predicted results of low
resolution features are recursively embedded into the learning framework. By
aggregating multi-level convolutional features in this efficient and flexible
manner, the proposed saliency model provides accurate salient object labeling.
Comprehensive experiments demonstrate that our method performs favorably
against state-of-the art approaches in terms of near all compared evaluation
metrics.
