Binary grid mask representation is broadly used in instance segmentation. A
representative instantiation is Mask R-CNN which predicts masks on a $28\times
28$ binary grid. Generally, a low-resolution grid is not sufficient to capture
the details, while a high-resolution grid dramatically increases the training
complexity. In this paper, we propose a new mask representation by applying the
discrete cosine transform(DCT) to encode the high-resolution binary grid mask
into a compact vector. Our method, termed DCT-Mask, could be easily integrated
into most pixel-based instance segmentation methods. Without any bells and
whistles, DCT-Mask yields significant gains on different frameworks, backbones,
datasets, and training schedules. It does not require any pre-processing or
pre-training, and almost no harm to the running speed. Especially, for
higher-quality annotations and more complex backbones, our method has a greater
improvement. Moreover, we analyze the performance of our method from the
perspective of the quality of mask representation. The main reason why DCT-Mask
works well is that it obtains a high-quality mask representation with low
complexity. Code is available at this https URL

本研究提出了一种新的掩模表示方法 DCT-Mask，通过在高分辨率二进制网格掩模中应用离散余弦变换编码成紧凑向量，进而容易地融入大多数基于像素的实例分割方法，并在不同框架、主干网络、数据集和训练时间表上仅仅稍有改进。