  Recent state-of-the-art one-stage instance segmentation model SOLO divides
the input image into a grid and directly predicts per grid cell object masks
with fully-convolutional networks, yielding comparably good performance as
traditional two-stage Mask R-CNN yet enjoying much simpler architecture and
higher efficiency. We observe SOLO generates similar masks for an object at
nearby grid cells, and these neighboring predictions can complement each other
as some may better segment certain object part, most of which are however
directly discarded by non-maximum-suppression. Motivated by the observed gap,
we develop a novel learning-based aggregation method that improves upon SOLO by
leveraging the rich neighboring information while maintaining the architectural
efficiency. The resulting model is named SODAR. Unlike the original per grid
cell object masks, SODAR is implicitly supervised to learn mask representations
that encode geometric structure of nearby objects and complement adjacent
representations with context. The aggregation method further includes two novel
designs: 1) a mask interpolation mechanism that enables the model to generate
much fewer mask representations by sharing neighboring representations among
nearby grid cells, and thus saves computation and memory; 2) a deformable
neighbour sampling mechanism that allows the model to adaptively adjust
neighbor sampling locations thus gathering mask representations with more
relevant context and achieving higher performance. SODAR significantly improves
the instance segmentation performance, e.g., it outperforms a SOLO model with
ResNet-101 backbone by 2.2 AP on COCO \texttt{test} set, with only about 3\%
additional computation. We further show consistent performance gain with the
SOLOv2 model.
