Current Camouflaged Object Detection (COD) methods primarily rely on a direct mapping from image to mask. However, due to the inherent semantic and structural gap between the image and its corresponding mask, the learned feature representations often exhibit poor generalization ability. To address this issue, we propose a novel intra-domain dual reconstruction framework, termed InDReCT, which reformulates the image-to-mask prediction as a cross-domain transfer task by simultaneously reconstructing both the input image and its corresponding mask...
