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.

我们提出了一种新颖的 “预训练、适应和检测” 方法，通过引入大型预训练模型，将从大量多模态数据中学习到的丰富知识直接转移到伪装对象检测任务中，使用轻量级并行适配器调整下游伪装对象检测任务的特征，通过在源任务上进行多任务适配器初始化和在目标任务上进行多任务调整，显著提高了我们模型的泛化能力。