Concealed Object Segmentation with Hierarchical Coherence Modeling
Fengyang Xiao Pan Zhang Chunming He Runze Hu Yutao Liu
Concealed object segmentation (COS) is a challenging task that involves
localizing and segmenting those concealed objects that are visually blended
with their surrounding environments. Despite achieving remarkable success,
existing COS segmenters still struggle to achieve complete segmentation results
in extremely concealed scenarios. In this paper, we propose a Hierarchical
Coherence Modeling (HCM) segmenter for COS, aiming to address this incomplete
segmentation limitation. In specific, HCM promotes feature coherence by
leveraging the intra-stage coherence and cross-stage coherence modules,
exploring feature correlations at both the single-stage and contextual levels.
Additionally, we introduce the reversible re-calibration decoder to detect
previously undetected parts in low-confidence regions, resulting in further
enhancing segmentation performance. Extensive experiments conducted on three
COS tasks, including camouflaged object detection, polyp image segmentation,
and transparent object detection, demonstrate the promising results achieved by
the proposed HCM segmenter.