Enhancing Prompt Generation with Adaptive Refinement for Camouflaged Object Detection

Foundation models, such as Segment Anything (SAM), have exhibited remarkable performance in conventional segmentation tasks, primarily due to their training on large-scale datasets. Nonetheless, challenges remain in specific downstream tasks, such as Camouflaged Object Detection (COD). Existing research primarily aims to enhance performance by integrating additional multimodal information derived from other foundation models. However, directly leveraging the information generated by these models may introduce additional biases due to domain shifts. To address this issue, we propose an Adaptive Refinement Module (ARM), which efficiently processes multimodal information and simultaneously enhances refined mask prompt. Furthermore, we construct an auxiliary embedding that effectively exploits the intermediate information generated during ARM, providing SAM with richer feature representations. Experimental results indicate that our proposed architecture surpasses most state-of-the-art (SOTA) models in the COD task, particularly excelling in structured target segmentation.