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High-quality instance segmentation has shown emerging importance in computervision. Without any refinement, DCT-Mask directly generates high-resolutionmasks by compressed vectors. To further refine masks obtained by compressedvectors, we propose for the first time a compressed vector based multi-stagerefinement framework. However, the vanilla combination does not bringsignificant gains, because changes in some elements of the DCT vector willaffect the prediction of the entire mask. Thus, we propose a simple and novelmethod named PatchDCT, which separates the mask decoded from a DCT vector intoseveral patches and refines each patch by the designed classifier andregressor. Specifically, the classifier is used to distinguish mixed patchesfrom all patches, and to correct previously mispredicted foreground andbackground patches. In contrast, the regressor is used for DCT vectorprediction of mixed patches, further refining the segmentation quality atboundary 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 onCOCO, 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 otherstate-of-the-art methods.

PatchDCT: Patch Refinement for High Quality Instance Segmentation