Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance
  Segmentation
Barış Can Çam Kemal Öksüz Fehmi Kahraman Zeynep Sonat Baltacı Sinan Kalkan Emre Akbaş
This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU)
as a new measure for positive-negative assignment of anchor boxes during
training of instance segmentation methods. Unlike conventional IoU measure or
its variants, which only consider the proximity of anchor and ground-truth
boxes; GmaIoU additionally takes into account the segmentation mask. This
enables GmaIoU to provide more accurate supervision during training. We
demonstrate the effectiveness of GmaIoU by replacing IoU with our GmaIoU in
ATSS, a state-of-the-art (SOTA) assigner. Then, we train YOLACT, a real-time
instance segmentation method, using our GmaIoU-based ATSS assigner. The
resulting YOLACT based on the GmaIoU assigner outperforms (i) ATSS with IoU by
$\sim 1.0-1.5$ mask AP, (ii) YOLACT with a fixed IoU threshold assigner by
$\sim 1.5-2$ mask AP over different image sizes and (iii) decreases the
inference time by $25 \%$ owing to using less anchors. Taking advantage of this
efficiency, we further devise GmaYOLACT, a faster and $+7$ mask AP points more
accurate detector than YOLACT. Our best model achieves $38.7$ mask AP at $26$
fps on COCO test-dev establishing a new state-of-the-art for real-time instance
segmentation.