  Currently, existing salient object detection methods based on convolutional
neural networks commonly resort to constructing discriminative networks to
aggregate high level and low level features. However, contextual information is
always not fully and reasonably utilized, which usually causes either the
absence of useful features or contamination of redundant features. To address
these issues, we propose a novel ladder context correlation complementary
network (LC3Net) in this paper, which is equipped with three crucial
components. At the beginning, we propose a filterable convolution block (FCB)
to assist the automatic collection of information on the diversity of initial
features, and it is simple yet practical. Besides, we propose a dense cross
module (DCM) to facilitate the intimate aggregation of different levels of
features by validly integrating semantic information and detailed information
of both adjacent and non-adjacent layers. Furthermore, we propose a
bidirectional compression decoder (BCD) to help the progressive shrinkage of
multi-scale features from coarse to fine by leveraging multiple pairs of
alternating top-down and bottom-up feature interaction flows. Extensive
experiments demonstrate the superiority of our method against 16
state-of-the-art methods.
