  Aiming to predict the complete shapes of partially occluded objects, amodal
segmentation is an important step towards visual intelligence. With crucial
significance, practical prior knowledge derives from sufficient training, while
limited amodal annotations pose challenges to achieve better performance. To
tackle this problem, utilizing the mighty priors accumulated in the foundation
model, we propose the first SAM-based amodal segmentation approach, PLUG.
Methodologically, a novel framework with hierarchical focus is presented to
better adapt the task characteristics and unleash the potential capabilities of
SAM. In the region level, due to the association and division in visible and
occluded areas, inmodal and amodal regions are assigned as the focuses of
distinct branches to avoid mutual disturbance. In the point level, we introduce
the concept of uncertainty to explicitly assist the model in identifying and
focusing on ambiguous points. Guided by the uncertainty map, a
computation-economic point loss is applied to improve the accuracy of predicted
boundaries. Experiments are conducted on several prominent datasets, and the
results show that our proposed method outperforms existing methods with large
margins. Even with fewer total parameters, our method still exhibits remarkable
advantages.
