Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset.

实例分割既需要像素级分类精度，又需要目标实例级的高级语义特征，这非常具有挑战性，而级联结构可以有效地改善这两个问题。为了充分利用检测和分割之间的关系，本文提出了一种联合的多任务级联结构，该结构不仅简单地将检测和分割这两个任务进行级联，而且将它们联合起来进行多阶段处理，特别是在mask分支的不同阶段集成信息。整个结构可以有效地利用每个阶段在检测和分割方面的优越特性，从而提高了掩模预测的质量。在全卷积网络（FCN）分支中引入了特征融合过程，有效地融合了高级和低级特征，以增强图片语义特征的上下文信息。实验证明在COCO数据集上有更好的结果。