  High-quality instance segmentation has shown emerging importance in computer
vision. Without any refinement, DCT-Mask directly generates high-resolution
masks by compressed vectors. To further refine masks obtained by compressed
vectors, we propose for the first time a compressed vector based multi-stage
refinement framework. However, the vanilla combination does not bring
significant gains, because changes in some elements of the DCT vector will
affect the prediction of the entire mask. Thus, we propose a simple and novel
method named PatchDCT, which separates the mask decoded from a DCT vector into
several patches and refines each patch by the designed classifier and
regressor. Specifically, the classifier is used to distinguish mixed patches
from all patches, and to correct previously mispredicted foreground and
background patches. In contrast, the regressor is used for DCT vector
prediction of mixed patches, further refining the segmentation quality at
boundary locations. Experiments on COCO show that our method achieves 2.0%,
3.2%, 4.5% AP and 3.4%, 5.3%, 7.0% Boundary AP improvements over Mask-RCNN on
COCO, LVIS, and Cityscapes, respectively. It also surpasses DCT-Mask by 0.7%,
1.1%, 1.3% AP and 0.9%, 1.7%, 4.2% Boundary AP on COCO, LVIS and Cityscapes.
Besides, the performance of PatchDCT is also competitive with other
state-of-the-art methods.
