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.

翻译：高质量实例分割在计算机视觉领域展现出日益重要的价值。DCT-Mask在不依赖任何精化操作的情况下，通过压缩向量直接生成高分辨率掩码。为对压缩向量生成的掩码进行精化，我们首次提出了基于压缩向量的多阶段精化框架。然而，简单的组合方式未能带来显著增益，因为DCT向量中某些元素的变化会影响整个掩码的预测。为此，我们提出一种简单新颖的方法——PatchDCT，该方法将从DCT向量解码得到的掩码划分为多个补丁，并通过设计的分类器与回归器对每个补丁进行精化。具体而言，分类器用于区分所有补丁中的混合补丁，并修正先前预测错误的前景与背景补丁；回归器则负责对混合补丁进行DCT向量预测，进一步优化边界位置的分割质量。在COCO数据集上的实验表明，相较于Mask-RCNN，本方法在COCO、LVIS和Cityscapes数据集上分别实现了2.0%、3.2%、4.5%的AP提升以及3.4%、5.3%、7.0%的边界AP提升；相较于DCT-Mask，在COCO、LVIS和Cityscapes数据集上分别取得0.7%、1.1%、1.3%的AP提升以及0.9%、1.7%、4.2%的边界AP提升。此外，PatchDCT的性能与其他前沿方法相比也具备竞争力。