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。

基于层次连贯建模的隐蔽物体分割

Concealed Object Segmentation with Hierarchical Coherence Modeling

Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment
objects well blended with surrounding environments using sparsely-annotated
data for model training. It remains a challenging task since (1) it is hard to
distinguish concealed objects from the background due to the intrinsic
similarity and (2) the sparsely-annotated training data only provide weak
supervision for model learning. In this paper, we propose a new WSCOS method to
address these two challenges. To tackle the intrinsic similarity challenge, we
design a multi-scale feature grouping module that first groups features at
different granularities and then aggregates these grouping results. By grouping
similar features together, it encourages segmentation coherence, helping obtain
complete segmentation results for both single and multiple-object images. For
the weak supervision challenge, we utilize the recently-proposed vision
foundation model, Segment Anything Model (SAM), and use the provided sparse
annotations as prompts to generate segmentation masks, which are used to train
the model. To alleviate the impact of low-quality segmentation masks, we
further propose a series of strategies, including multi-augmentation result
ensemble, entropy-based pixel-level weighting, and entropy-based image-level
selection. These strategies help provide more reliable supervision to train the
segmentation model. We verify the effectiveness of our method on various WSCOS
tasks, and experiments demonstrate that our method achieves state-of-the-art
performance on these tasks.

提出一种用于弱监督下的隐蔽物体分割的方法，利用多尺度特征分组模块处理内在相似性问题，并采用 Segment Anything Model 和基于熵的像素加权策略进行模型训练，同时应用基于熵的图像选择策略来减轻低质量分割蒙版的影响，实验证明该方法在各种 WSCOS 任务上取得了最先进的性能。

基于 SAM 伪标记和多尺度特征分组的弱监督隐蔽目标分割

Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo  Labeling and Multi-scale Feature Grouping

Concealed scene understanding (CSU) is a hot computer vision topic aiming to
perceive objects with camouflaged properties. The current boom in its advanced
techniques and novel applications makes it timely to provide an up-to-date
survey to enable researchers to understand the global picture of the CSU field,
including both current achievements and major challenges. This paper makes four
contributions: (1) For the first time, we present a comprehensive survey of the
deep learning techniques oriented at CSU, including a background with its
taxonomy, task-unique challenges, and a review of its developments in the deep
learning era via surveying existing datasets and deep techniques. (2) For a
quantitative comparison of the state-of-the-art, we contribute the largest and
latest benchmark for Concealed Object Segmentation (COS). (3) To evaluate the
transferability of deep CSU in practical scenarios, we re-organize the largest
concealed defect segmentation dataset termed CDS2K with the hard cases from
diversified industrial scenarios, on which we construct a comprehensive
benchmark. (4) We discuss open problems and potential research directions for
this community. Our code and datasets are available at
this https URL, which will be updated continuously to watch
and summarize the advancements in this rapidly evolving field.

该研究论文综述了计算机视觉领域热门主题之一 —— 隐蔽场景理解的深度学习技术，并建立了两个大型数据集以推动该领域的发展。