Correctness of instance segmentation constitutes counting the number of
objects, correctly localizing all predictions and classifying each localized
prediction. Average Precision is the de-facto metric used to measure all these
constituents of segmentation. However, this metric does not penalize duplicate
predictions in the high-recall range, and cannot distinguish instances that are
localized correctly but categorized incorrectly. This weakness has
inadvertently led to network designs that achieve significant gains in AP but
also introduce a large number of false positives. We therefore cannot rely on
AP to choose a model that provides an optimal tradeoff between false positives
and high recall. To resolve this dilemma, we review alternative metrics in the
literature and propose two new measures to explicitly measure the amount of
both spatial and categorical duplicate predictions. We also propose a Semantic
Sorting and NMS module to remove these duplicates based on a pixel occupancy
matching scheme. Experiments show that modern segmentation networks have
significant gains in AP, but also contain a considerable amount of duplicates.
Our Semantic Sorting and NMS can be added as a plug-and-play module to mitigate
hedged predictions and preserve AP.

本文提出两个新的度量标准来明确测量空间和类别重复预测的数量，并提出基于像素占用匹配方案的语义排序和非极大值抑制模块，以减轻权衡预测并保留平均精度。