  Much of the recent efforts on salient object detection (SOD) have 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 better use the rich feature hierarchies in deep
networks and enhance the side predictions, we propose the regularized dense
connections, which attentively promote informative features and suppress
non-informative ones from all feature pyramids. A novel multi-level RoIAlign
based decoder is introduced to adaptively aggregate multi-level features for
better mask predictions. Such strategies can be well-encapsulated into the Mask
R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that
our design significantly outperforms existing \sArt 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.
