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Camouflaged object detection (COD), aiming to segment camouflaged objectswhich exhibit similar patterns with the background, is a challenging task. Mostexisting works are dedicated to establishing specialized modules to identifycamouflaged objects with complete and fine details, while the boundary can notbe well located for the lack of object-related semantics. In this paper, wepropose a novel ``pre-train, adapt and detect" paradigm to detect camouflagedobjects. By introducing a large pre-trained model, abundant knowledge learnedfrom massive multi-modal data can be directly transferred to COD. A lightweightparallel adapter is inserted to adjust the features suitable for the downstreamCOD task. Extensive experiments on four challenging benchmark datasetsdemonstrate that our method outperforms existing state-of-the-art COD models bylarge margins. Moreover, we design a multi-task learning scheme for tuning theadapter to exploit the shareable knowledge across different semantic classes.Comprehensive experimental results showed that the generalization ability ofour model can be substantially improved with multi-task adapter initializationon source tasks and multi-task adaptation on target tasks.

Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection