  Instance perception tasks (object detection, instance segmentation, pose
estimation, counting) play a key role in industrial applications of visual
models. As supervised learning methods suffer from high labeling cost, few-shot
learning methods which effectively learn from a limited number of labeled
examples are desired. Existing few-shot learning methods primarily focus on a
restricted set of tasks, presumably due to the challenges involved in designing
a generic model capable of representing diverse tasks in a unified manner. In
this paper, we propose UniFS, a universal few-shot instance perception model
that unifies a wide range of instance perception tasks by reformulating them
into a dynamic point representation learning framework. Additionally, we
propose Structure-Aware Point Learning (SAPL) to exploit the higher-order
structural relationship among points to further enhance representation
learning. Our approach makes minimal assumptions about the tasks, yet it
achieves competitive results compared to highly specialized and well optimized
specialist models. Codes and data are available at
https://github.com/jin-s13/UniFS.
