To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: https://git.io/AdelaiDet

本论文提出一种简单的单级实例分割框架，即基于掩码编码的实例分割（MEInst），通过将二维掩码提炼成紧凑且固定维度的表征向量，使得实例分割任务能够被纳入单级边界框检测器中，从而实现了一种简单而高效的实例分割方法，取得了36.4％的掩码AP，在MS-COCO基准测试中取得了竞争性的性能。

单次拍摄物体实例分割的口罩编码