  Feature matters for salient object detection. Existing methods mainly focus
on designing a sophisticated structure to incorporate multi-level features and
filter out cluttered features. We present Progressive Feature Polishing Network
(PFPN), a simple yet effective framework to progressively polish the
multi-level features to be more accurate and representative. By employing
multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach
is able to detect salient objects with fine details without any
post-processing. A FPM parallelly updates the features of each level by
directly incorporating all higher level context information. Moreover, it can
keep the dimensions and hierarchical structures of the feature maps, which
makes it flexible to be integrated with any CNN-based models. Empirical
experiments show that our results are monotonically getting better with
increasing number of FPMs. Without bells and whistles, PFPN outperforms the
state-of-the-art methods significantly on five benchmark datasets under various
evaluation metrics.
