  Remarkable progress has been made in object instance detection and
segmentation in recent years. However, existing state-of-the-art methods are
mostly evaluated with fairly balanced and class-limited benchmarks, such as
Microsoft COCO dataset [8]. In this report, we investigate the performance drop
phenomenon of state-of-the-art two-stage instance segmentation models when
processing extreme long-tail training data based on the LVIS [5] dataset, and
find a major cause is the inaccurate classification of object proposals. Based
on this observation, we propose to calibrate the prediction of classification
head to improve recognition performance for the tail classes. Without much
additional cost and modification of the detection model architecture, our
calibration method improves the performance of the baseline by a large margin
on the tail classes. Codes will be available. Importantly, after the
submission, we find significant improvement can be further achieved by
modifying the calibration head, which we will update later.
