  High-accuracy Dichotomous Image Segmentation (DIS) aims to pinpoint
category-agnostic foreground objects from natural scenes. The main challenge
for DIS involves identifying the highly accurate dominant area while rendering
detailed object structure. However, directly using a general encoder-decoder
architecture may result in an oversupply of high-level features and neglect the
shallow spatial information necessary for partitioning meticulous structures.
To fill this gap, we introduce a novel Unite-Divide-Unite Network (UDUN} that
restructures and bipartitely arranges complementary features to simultaneously
boost the effectiveness of trunk and structure identification. The proposed
UDUN proceeds from several strengths. First, a dual-size input feeds into the
shared backbone to produce more holistic and detailed features while keeping
the model lightweight. Second, a simple Divide-and-Conquer Module (DCM) is
proposed to decouple multiscale low- and high-level features into our structure
decoder and trunk decoder to obtain structure and trunk information
respectively. Moreover, we design a Trunk-Structure Aggregation module (TSA) in
our union decoder that performs cascade integration for uniform high-accuracy
segmentation. As a result, UDUN performs favorably against state-of-the-art
competitors in all six evaluation metrics on overall DIS-TE, i.e., achieving
0.772 weighted F-measure and 977 HCE. Using 1024*1024 input, our model enables
real-time inference at 65.3 fps with ResNet-18.
