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.

本文提出了一种用于隐蔽物体分割的分层一致性建模器，以解决现有分割器在极度隐蔽场景中无法完全实现分割结果的问题。通过利用单一阶段和上下文级别的特征相关性，HCM 分割器在提升特征一致性方面起到了积极作用。此外，引入可逆重新校准解码器，以检测低置信区域中先前未检测到的部分，进一步提高了分割性能。在包括伪装物体检测、息肉图像分割和透明物体检测在内的三个隐蔽物体分割任务上进行的大量实验证明了 HCM 分割器的良好效果。