Existing weakly-supervised segmentation approaches based on image-level annotations may focus on the most activated region in the image and tend to identify only part of the target object. Intuitively, high-level semantics among objects of the same category in different images could help to recognize corresponding activated regions of the query. In this study, a scheme called Cycle-Consistency of Semantics Network (CyCSNet) is proposed, which can enhance the activation of the potential inactive regions of the target object by utilizing the cycle-consistent semantics from images of the same category in the training set. Moreover, a Dynamic Correlation Feature Selection (DCFS) algorithm is derived to reduce the noise from pixel-wise samples of low relevance for better training. Experiments on the PASCAL VOC 2012 dataset show that the proposed CyCSNet achieves competitive results compared with state-of-the-art weakly-supervised segmentation approaches.