  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.
