Efficient Architecture Search for Real-Time Instance Segmentation

Abstract

Traditional CNN-based training for instance segmentation is time-consuming owing to large datasets and complex network modules, making direct searching of architecture challenging. In this paper, we introduce an efficient framework, named EASInst. It can discover practical backbone and encoder architectures for the improved sparse activation instance segmentation model. Specifically, we construct a supernet for both backbone and encoder modules of SparseInst based on a differentiable method. In addition, kernel sharing mask and channel pruning technology are employed. Moreover, Taylor-Loss and a novel DY-Loss are devised for instance segmentation to improve the accuracy. Experiments show that the searched architectures outperform the existing Resnet-based real-time instance segmentation methods, which achieve 38.5 mAP with 39.5 FPS on COCO test-dev set.