Abstract:Aiming at the difficult balance between the global receptive field and efficient computation and unclear details of image reconstruction, an attribute guided network based on CNN-Mamba (CMANet) is proposed. Firstly, when the model is reconstructed, attribute information is introduced and interrelationships among these attributes are considered, which helps the model to improve the reliability and accuracy of the whole reconstruction process. Secondly, the hourglass state space module is introduced to explore the key features of face images and maintain the advantage of linear complexity in long-distance dependency modeling. Finally, an adaptive Mamba fusion module is introduced. When image features learn long-distance dependencies in multiple directions, attributes are adaptively supplemented in different directions, and features supplemented in different directions are adaptively fused, making the model more flexible and efficient in processing diverse images. A large number of experiments prove the superiority of the proposed method.