AFINet: Camouflaged object detection via Attention Fusion and Interaction Network

Since the camouflaged objects share very similar colors and textures with the surroundings, there is still a great challenge in accurately locating and segmenting target objects with the varying sizes and shapes in different scenes. In this paper, we propose a novel Attention Fusion and Interaction network (AFINet) to detect the camouflaged objects by exploring the cross -level complementary information. Specifically, we first propose a Multi -Attention Interaction (MAI) module to fuse the cross -level features containing different characteristics by the attention interaction, thereby fully making use of the specific and complementary information from different levels to deal with scale variation. Furthermore, we design a Location and Boundary Guidance (LBG) module to make each side -output feature aware of where to learn, which can avoid the disturbances of the noncamouflaged regions by distinguishing the subtle differences. Comprehensive experiments and comparisons are conducted on four widely used benchmark datasets, demonstrating that the proposed network achieves stateof-the-art performance. The code and prediction maps will be available at https://github.com/ZhangQing0329/ AFINet.