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.

翻译：弱监督隐蔽目标分割（Weakly-Supervised Concealed Object Segmentation, WSCOS）旨在利用稀疏标注数据训练模型，以分割与周围环境高度融合的目标。该任务具有较大挑战性，原因在于：(1) 由于内在相似性，隐蔽目标与背景难以区分；(2) 稀疏标注的训练数据只能为模型学习提供弱监督信号。本文针对上述两个挑战提出一种新的WSCOS方法。为解决内在相似性挑战，我们设计了多尺度特征分组模块，该模块首先对不同粒度的特征进行分组，随后聚合这些分组结果。通过将相似特征聚合在一起，该方法可增强分割连贯性，有助于在单目标和多目标图像中获取完整的分割结果。针对弱监督挑战，我们利用近期提出的视觉基础模型——Segment Anything Model（SAM），并将提供的稀疏标注作为提示生成分割掩码，用于训练模型。为缓解低质量分割掩码的影响，我们进一步提出一系列策略，包括多增强结果集成、基于熵的像素级加权以及基于熵的图像级筛选。这些策略可为训练分割模型提供更可靠的监督信号。我们在多种WSCOS任务上验证了方法的有效性，实验结果表明，该方法在这些任务上取得了最先进的性能。