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.

翻译：隐蔽目标分割（COS）是一项具有挑战性的任务，涉及定位和分割那些与周围环境视觉融合的隐蔽目标。尽管现有COS分割器取得了显著成功，但在极端隐蔽场景下仍难以实现完整的分割结果。本文提出了一种基于分层一致性建模的分割器HCM用于COS，旨在解决这一不完整分割的局限性。具体而言，HCM通过利用层内一致性与跨层一致性模块，在单层级和上下文层面探索特征相关性，从而增强特征一致性。此外，我们引入了可逆重标定解码器，用于检测低置信度区域中先前未检测到的部分，进一步提升了分割性能。在三个COS任务（包括伪装目标检测、息肉图像分割和透明目标检测）上进行的广泛实验表明，所提出的HCM分割器取得了良好的结果。