  Excellent performance has been achieved on instance segmentation but the
quality on the boundary area remains unsatisfactory, which leads to a rising
attention on boundary refinement. For practical use, an ideal post-processing
refinement scheme are required to be accurate, generic and efficient. However,
most of existing approaches propose pixel-wise refinement, which either
introduce a massive computation cost or design specifically for different
backbone models. Contour-based models are efficient and generic to be
incorporated with any existing segmentation methods, but they often generate
over-smoothed contour and tend to fail on corner areas. In this paper, we
propose an efficient contour-based boundary refinement approach, named
SharpContour, to tackle the segmentation of boundary area. We design a novel
contour evolution process together with an Instance-aware Point Classifier. Our
method deforms the contour iteratively by updating offsets in a discrete
manner. Differing from existing contour evolution methods, SharpContour
estimates each offset more independently so that it predicts much sharper and
accurate contours. Notably, our method is generic to seamlessly work with
diverse existing models with a small computational cost. Experiments show that
SharpContour achieves competitive gains whilst preserving high efficiency
