Multi-Clue Sliding Window Attention for Camouflaged Object Detection

The aim of camouflaged object detection (COD) is to discern concealed objects within the background. Due to issues such as high similarity to the surrounding environment, small size, occlusions, COD is considered a highly challenging task. In this paper, we propose a novel COD framework, named multi-clue sliding window attention network (MCSWA-Net), stressing in utilizing prior knowledge at different semantic levels to guide the detection of camouflaged objects via multi-scale sliding window attention (MSWA). To this end, we first devise the dynamic local detail capture (DLC) module and the global interactive decoder (GID) module to generate both local and global guidance clues. Particularly, each block of the DLC module produces local prior clue by processing corresponding image features at each stage from the encoder. And the GID module fuses all adjacent encoder features, generates global prior clue by combining fusion features of multi-semantic levels. Further, to make full use of prior clues guiding the detection of camouflaged objects at multi-semantic levels, we design the multi-scale guidance attention fusion (MAF) module and use two prior clues to refine the image features via the group fusion and the MSWA separately. Experiments conducted on four COD benchmark datasets, and results demonstrate that our MCSWA-Net is superior to state-of-the-art (SOTA) COD methods. In addition, we explore the detection capabilities of our MCSWA-Net for the downstream vision tasks related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, and industrial defect detection. Experimental results show the proposed method has high degree of generality.