Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related semantics. In this paper, we propose a novel ``pre-train, adapt and detect" paradigm to detect camouflaged objects. By introducing a large pre-trained model, abundant knowledge learned from massive multi-modal data can be directly transferred to COD. A lightweight parallel adapter is inserted to adjust the features suitable for the downstream COD task. Extensive experiments on four challenging benchmark datasets demonstrate that our method outperforms existing state-of-the-art COD models by large margins. Moreover, we design a multi-task learning scheme for tuning the adapter to exploit the shareable knowledge across different semantic classes. Comprehensive experimental results showed that the generalization ability of our model can be substantially improved with multi-task adapter initialization on source tasks and multi-task adaptation on target tasks.

翻译：伪装目标检测（COD）旨在分割与背景呈现相似模式的伪装目标，是一项具有挑战性的任务。现有研究大多致力于构建专用模块来识别具有完整精细细节的伪装目标，但由于缺乏目标相关语义信息，其边界定位效果不佳。本文提出一种新颖的“预训练、适配与检测”范式来检测伪装目标。通过引入大规模预训练模型，可从海量多模态数据中习得的丰富知识直接迁移至COD任务。我们设计了一种轻量级并行适配器，用于调整特征使之适配下游COD任务。在四个具有挑战性的基准数据集上的大量实验表明，本方法显著优于现有最先进的COD模型。此外，我们设计了一种多任务学习方案来微调配适器，以挖掘不同语义类别间的共享知识。综合实验结果表明，通过在源任务上进行多任务适配器初始化并在目标任务上进行多任务适配，本模型的泛化能力可获得显著提升。