  Recent methods for long-tailed instance segmentation still struggle on rare
object classes with few training data. We propose a simple yet effective
method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the
data scarcity issue by augmenting the feature space especially for rare
classes. Both the Feature Augmentation (FA) and feature sampling components are
adaptive to the actual training status -- FA is informed by the feature mean
and variance of observed real samples from past iterations, and we sample the
generated virtual features in a loss-adapted manner to avoid over-fitting. FASA
does not require any elaborate loss design, and removes the need for
inter-class transfer learning that often involves large cost and
manually-defined head/tail class groups. We show FASA is a fast, generic method
that can be easily plugged into standard or long-tailed segmentation
frameworks, with consistent performance gains and little added cost. FASA is
also applicable to other tasks like long-tailed classification with
state-of-the-art performance. Code will be released.
