Despite significant progress in instance segmentation, recent solutions still fall short of boundary accuracy especially for overlapping instances of the same category. In this paper, we propose a novel boundary-enhanced instance segmentation (BEIS) framework that explicitly models the feature relationships across object boundaries for high-quality instance segmentation. Specifically, BEIS generates boundary-enhanced features using both intra-mask and cross-image boundary discrimination learning. The intra-mask boundary discrimination learning (IBDL) employs pixel-level discrimination learning to disentangle pixel representations along boundaries. The cross-image boundary discrimination learning (CBDL) learns a boundary-aware feature bank from training data to further boost the performance. Thus, CBDL can take advantage of boundary relations across images to enhance the quality of segmented boundaries. To focus on hard-to-segment boundaries, we propose an adaptive sampling strategy to automatically construct discriminative pairs in regions with high possibilities of confusion. Extensive experiments show BEIS outperforms on various datasets.