EffSeg: Efficient Fine-Grained Instance Segmentation using
  Structure-Preserving Sparsity
Cédric Picron Tinne Tuytelaars
Many two-stage instance segmentation heads predict a coarse 28x28 mask per
instance, which is insufficient to capture the fine-grained details of many
objects. To address this issue, PointRend and RefineMask predict a 112x112
segmentation mask resulting in higher quality segmentations. Both methods
however have limitations by either not having access to neighboring features
(PointRend) or by performing computation at all spatial locations instead of
sparsely (RefineMask). In this work, we propose EffSeg performing fine-grained
instance segmentation in an efficient way by using our Structure-Preserving
Sparsity (SPS) method based on separately storing the active features, the
passive features and a dense 2D index map containing the feature indices. The
goal of the index map is to preserve the 2D spatial configuration or structure
between the features such that any 2D operation can still be performed. EffSeg
achieves similar performance on COCO compared to RefineMask, while reducing the
number of FLOPs by 71% and increasing the FPS by 29%. Code will be released.