Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods.

翻译：大多数伪装目标检测方法严重依赖掩码标注，而获取这些标注既耗时又费力。现有的弱监督伪装目标检测方法性能显著低于全监督方法，且难以同时支持所有现有类型的伪装目标标注，包括涂鸦、边界框和点标注。即使是Segment Anything Model，在处理弱监督伪装目标检测时仍存在问题，通常面临涂鸦标签的提示兼容性、极端响应、语义错误响应以及不稳定的特征表示等挑战，导致在伪装场景中产生不理想的结果。为缓解这些问题，本文提出一个统一的伪装目标检测框架，称为SAM-COD，能够支持任意的弱监督标签。我们的SAM-COD采用提示适配器，基于SAM处理涂鸦作为提示。同时，我们引入响应过滤器和语义匹配器模块，以提升SAM在伪装目标检测提示下获得的掩码质量。为减轻不准确掩码预测的负面影响，采用一种新的提示自适应知识蒸馏策略来确保可靠的特征表示。为验证方法的有效性，我们在三个主流伪装目标检测基准上进行了广泛的实证实验。结果表明，我们的方法优于当前最先进的弱监督乃至全监督方法。