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

• URL: http://arxiv.org/abs/2307.14052v1
• Date: Wed, 26 Jul 2023 09:04:35 GMT
• Title: Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation
• Authors: Jialun Pei, Zhangjun Zhou, Yueming Jin, He Tang, Pheng-Ann Heng
• Abstract summary: High-accuracy Dichotomous Image rendering (DIS) aims to pinpoint category-agnostic foreground objects from natural scenes. We introduce a novel Unite-Divide-Unite Network (UDUN) that restructures and bipartitely arranges complementary features to boost the effectiveness of trunk and structure identification. Using 1024*1024 input, our model enables real-time inference at 65.3 fps with ResNet-18.
• Score: 48.995367430746086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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