Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

Much of the recent efforts on salient object detection (SOD) has been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To make better use of the rich feature hierarchies in deep networks, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids, to enhance the side predictions. A novel multi-level RoIAlign based decoder is introduced as well to adaptively aggregate multi-level features for better mask predictions. Such good strategies can be well-encapsulated into the Mask-RCNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing state-of-the-art competitors by 6.3% (58.6% vs 52.3%) in terms of the AP metric. The code is available at https://github.com/yuhuan-wu/RDPNet.