  In this paper, we propose a conceptually novel, efficient, and fully
convolutional framework for real-time instance segmentation. Previously, most
instance segmentation methods heavily rely on object detection and perform mask
prediction based on bounding boxes or dense centers. In contrast, we propose a
sparse set of instance activation maps, as a new object representation, to
highlight informative regions for each foreground object. Then instance-level
features are obtained by aggregating features according to the highlighted
regions for recognition and segmentation. Moreover, based on bipartite
matching, the instance activation maps can predict objects in a one-to-one
style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to
the simple yet effective designs with instance activation maps, SparseInst has
extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO
benchmark, which significantly outperforms the counterparts in terms of speed
and accuracy. Code and models are available at
https://github.com/hustvl/SparseInst.
