MULTI-SCALE CAPSULE ATTENTION-BASED SALIENT OBJECT DETECTION WITH MULTI-CROSSED LAYER CONNECTIONS

With the popularization of convolutional networks being used for saliency models, saliency detection performance has achieved significant improvement. However, how to integrate accurate and crucial features for modeling saliency is still underexplored. In this paper, we present CapSalNet, which includes a multi-scale Capsule attention module and multi-crossed layer connections for Salient object detection. We first propose a novel capsule attention model, which integrates multi-scale contextual information with dynamic routing. Then, our model adaptively learns to aggregate multi-level features by using multi-crossed skip-layer connections. Finally, the predicted results are efficiently fused to generate the final saliency map in a coarse-to-fine manner. Comprehensive experiments on four benchmark datasets demonstrate that our proposed algorithm outperforms existing state-of-the-art approaches.