Abstract
Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask annotations is time-consuming and labor-intensive. Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance. Even for the Segment Anything Model (SAM), there are still challenges in handling weakly-supervised camouflage object detection (WSCOD), such as: a. non-camouflage target responses, b. local responses, c. extreme responses, and d. lack of refined boundary awareness, which leads to unsatisfactory results in camouflage scenes. To alleviate these issues, we propose a frequency-aware and contrastive learning-based WSCOD framework in this paper, named FCL-COD. To mitigate the problem of non-camouflaged object responses, we propose the Frequency-aware Low-rank Adaptation (FoRA) method, which incorporates frequency-aware camouflage scene knowledge into SAM. To overcome the challenges of local and extreme responses, we introduce a gradient-aware contrastive learning approach that effectively delineates precise foreground-background boundaries. Additionally, to address the lack of refined boundary perception, we present a multi-scale frequency-aware representation learning strategy that facilitates the modeling of more refined boundaries. We validate the effectiveness of our approach through extensive empirical experiments on three widely recognized COD benchmarks. The results confirm that our method surpasses both state-of-the-art weakly supervised and even fully supervised techniques.
Abstract (translated)
现有的伪装目标检测（COD）方法通常依赖于由掩码标注指导的全监督学习。然而，获取掩码标注耗时且劳动密集。与全监督方法相比，现有的弱监督COD方法性能显著较差。即便是Segment Anything Model（SAM），在处理弱监督伪装目标检测（WSCOD）时仍面临挑战，例如：a. 非伪装目标响应、b. 局部响应、c. 极端响应以及d. 缺乏精细化边界感知，导致在伪装场景中结果不理想。为缓解这些问题，本文提出一种基于频域感知与对比学习的WSCOD框架，命名为FCL-COD。为减轻非伪装对象响应问题，我们提出了频域感知低秩适应（FoRA）方法，将频域感知的伪装场景知识融入SAM。为克服局部与极端响应的挑战，我们引入了梯度感知对比学习方法，能有效勾勒精确的前景-背景边界。此外，为应对精细化边界感知缺失的问题，我们提出了多尺度频域表征学习策略，促进更精细化边界建模。我们在三个公认的COD基准数据集上通过大量实证实验验证了方法的有效性，结果证实我们的方法超越了最先进的弱监督乃至全监督技术。
URL
https://arxiv.org/abs/2603.22969
