  Most existing object instance detection and segmentation models only work
well on fairly balanced benchmarks where per-category training sample numbers
are comparable, such as COCO. They tend to suffer performance drop on realistic
datasets that are usually long-tailed. This work aims to study and address such
open challenges. Specifically, we systematically investigate performance drop
of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the
recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate
classification of object proposals. Based on such an observation, we first
consider various techniques for improving long-tail classification performance
which indeed enhance instance segmentation results. We then propose a simple
calibration framework to more effectively alleviate classification head bias
with a bi-level class balanced sampling approach. Without bells and whistles,
it significantly boosts the performance of instance segmentation for tail
classes on the recent LVIS dataset and our sampled COCO-LT dataset. Our
analysis provides useful insights for solving long-tail instance detection and
segmentation problems, and the straightforward \emph{SimCal} method can serve
as a simple but strong baseline. With the method we have won the 2019 LVIS
challenge. Codes and models are available at https://github.com/twangnh/SimCal.
