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High-accuracy Dichotomous Image Segmentation (DIS) aims to pinpointcategory-agnostic foreground objects from natural scenes. The main challengefor DIS involves identifying the highly accurate dominant area while renderingdetailed object structure. However, directly using a general encoder-decoderarchitecture may result in an oversupply of high-level features and neglect theshallow spatial information necessary for partitioning meticulous structures.To fill this gap, we introduce a novel Unite-Divide-Unite Network (UDUN} thatrestructures and bipartitely arranges complementary features to simultaneouslyboost the effectiveness of trunk and structure identification. The proposedUDUN proceeds from several strengths. First, a dual-size input feeds into theshared backbone to produce more holistic and detailed features while keepingthe model lightweight. Second, a simple Divide-and-Conquer Module (DCM) isproposed to decouple multiscale low- and high-level features into our structuredecoder and trunk decoder to obtain structure and trunk informationrespectively. Moreover, we design a Trunk-Structure Aggregation module (TSA) inour union decoder that performs cascade integration for uniform high-accuracysegmentation. As a result, UDUN performs favorably against state-of-the-artcompetitors in all six evaluation metrics on overall DIS-TE, i.e., achieving0.772 weighted F-measure and 977 HCE. Using 1024*1024 input, our model enablesreal-time inference at 65.3 fps with ResNet-18.

Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation