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 will be released soon.

通过将多个实例感知任务转化为动态点表示学习框架，UniFS 提出了一种通用的少样本实例感知模型，并提出 Structure-Aware Point Learning (SAPL) 以进一步增强表示学习。