Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Although progress has been made recently by combining prototype-based metric learning, existing methods still face two main challenges. First, various intra-class objects between the support and query images or semantically similar inter-class objects can seriously harm the segmentation performance due to their poor feature representations. Second, the latent novel classes are treated as the background in most methods, leading to a learning bias, whereby these novel classes are difficult to correctly segment as foreground. To solve these problems, we propose a dual-branch learning method. The class-specific branch encourages representations of objects to be more distinguishable by increasing the inter-class distance while decreasing the intra-class distance. In parallel, the class-agnostic branch focuses on minimizing the foreground class feature distribution and maximizing the features between the foreground and background, thus increasing the generalizability to novel classes in the test stage. Furthermore, to obtain more representative features, pixel-level and prototype-level semantic learning are both involved in the two branches. The method is evaluated on PASCAL-5^i~1 -shot, PASCAL-5^i~5 -shot, COCO-20^i~1 -shot, and COCO-20^i~5 -shot, and extensive experiments show that our approach is effective for few-shot semantic segmentation despite its simplicity.