  We present a high-performance method that can achieve mask-level instance
segmentation with only bounding-box annotations for training. While this
setting has been studied in the literature, here we show significantly stronger
performance with a simple design (e.g., dramatically improving previous best
reported mask AP of 21.1% in Hsu et al. (2019) to 31.6% on the COCO dataset).
Our core idea is to redesign the loss of learning masks in instance
segmentation, with no modification to the segmentation network itself. The new
loss functions can supervise the mask training without relying on mask
annotations. This is made possible with two loss terms, namely, 1) a surrogate
term that minimizes the discrepancy between the projections of the ground-truth
box and the predicted mask; 2) a pairwise loss that can exploit the prior that
proximal pixels with similar colors are very likely to have the same category
label. Experiments demonstrate that the redesigned mask loss can yield
surprisingly high-quality instance masks with only box annotations. For
example, without using any mask annotations, with a ResNet-101 backbone and 3x
training schedule, we achieve 33.2% mask AP on COCO test-dev split (vs. 39.1%
of the fully supervised counterpart). Our excellent experiment results on COCO
and Pascal VOC indicate that our method dramatically narrows the performance
gap between weakly and fully supervised instance segmentation.
  Code is available at: https://git.io/AdelaiDet
