Recently, existing FCNs-based methods have shown their advantages in processing object boundaries. However, these methods still suffer from false object interference, which appears in saliency predictions. To solve th...
详细信息
Recently, existing FCNs-based methods have shown their advantages in processing object boundaries. However, these methods still suffer from false object interference, which appears in saliency predictions. To solve this problem, an edge-interior feature fusion (EIFF) framework is proposed, which consists of an internal-boundary decoupled generation structure with receptive field enlargement and attention mechanism enhancement, and a salient feature refinement module. Specifically, the framework first learns edge features and interior features through an internal-boundary decoupling generation network, which is supervised by labels obtained by decoupling ground-truth through an image erosion algorithm. Then, feature refinement module (FRM) is designed to purify the coarse prediction by focusing on the ambiguous regions through a mining strategy to generate the final saliency map. To compensate for shortcomings of the BCE and IU loss, we also introduce a weighted loss to guide our model to focus more on the error-prone parts. Experimental results on five benchmark datasets demonstrate that the proposed method performs favorably against 19 state-of-the-art approaches under four standard metrics.
暂无评论