  Instance segmentation, a cornerstone task in computer vision, has
wide-ranging applications in diverse industries. The advent of deep learning
and artificial intelligence has underscored the criticality of training
effective models, particularly in data-scarce scenarios - a concern that
resonates in both academic and industrial circles. A significant impediment in
this domain is the resource-intensive nature of procuring high-quality,
annotated data for instance segmentation, a hurdle that amplifies the challenge
of developing robust models under resource constraints. In this context, the
strategic integration of a visual prior into the training dataset emerges as a
potential solution to enhance congruity with the testing data distribution,
consequently reducing the dependency on computational resources and the need
for highly complex models. However, effectively embedding a visual prior into
the learning process remains a complex endeavor. Addressing this challenge, we
introduce the MISS (Memory-efficient Instance Segmentation System) framework.
MISS leverages visual inductive prior flow propagation, integrating intrinsic
prior knowledge from the Synergy-basketball dataset at various stages: data
preprocessing, augmentation, training, and inference. Our empirical evaluations
underscore the efficacy of MISS, demonstrating commendable performance in
scenarios characterized by limited data availability and memory constraints.
