论文标题
掩码编码单镜头实例细分
Mask Encoding for Single Shot Instance Segmentation
论文作者
论文摘要
迄今为止，实例分割由twostage方法主导，这是由蒙版R-CNN启用的。相比之下，一阶段的替代方案无法与Mask AP中的Mask R-CNN竞争，这主要是由于很难紧凑地代表面具，这使得设计一阶段方法的设计非常具有挑战性。在这项工作中，我们提出了一个简单的单身肖特实例分割框架，称为“掩码”编码基于实例分割（meinst）。我没有直接预测二维掩码，而是将其提炼成紧凑而固定的表示矢量，该矢量允许实例分割任务被整合到一个阶段的边界盒检测器中，并导致简单而有效的实例段semmentation Sexmentation semmentation semmentation框架。提出的一阶段Meinst在MS-Coco基准上使用单模型（Resnex-101-FPN主链）和单尺度测试实现了36.4％的蒙版AP。我们表明，更简单，更灵活的单阶段实例细分方法也可以实现竞争性能。该框架可以轻松适应其他实例级识别任务。代码可在以下网址找到：https：//git.io/adelaidet
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
