  Contour-based instance segmentation methods include one-stage and multi-stage
schemes. These approaches achieve remarkable performance. However, they have to
define plenty of points to segment precise masks, which leads to high
complexity. We follow this issue and present a single-shot method, called
\textbf{VeinMask}, for achieving competitive performance in low design
complexity. Concretely, we observe that the leaf locates coarse margins via
major veins and grows minor veins to refine twisty parts, which makes it
possible to cover any objects accurately. Meanwhile, major and minor veins
share the same growth mode, which avoids modeling them separately and ensures
model simplicity. Considering the superiorities above, we propose VeinMask to
formulate the instance segmentation problem as the simulation of the vein
growth process and to predict the major and minor veins in polar coordinates.
  Besides, centroidness is introduced for instance segmentation tasks to help
suppress low-quality instances. Furthermore, a surroundings cross-correlation
sensitive (SCCS) module is designed to enhance the feature expression by
utilizing the surroundings of each pixel. Additionally, a Residual IoU (R-IoU)
loss is formulated to supervise the regression tasks of major and minor veins
effectively. Experiments demonstrate that VeinMask performs much better than
other contour-based methods in low design complexity. Particularly, our method
outperforms existing one-stage contour-based methods on the COCO dataset with
almost half the design complexity.
