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

Understand

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.