FASSST: Fast Attention Based Single-Stage Segmentation Net for Real-Time Instance Segmentation

Yuan Cheng, Rui Lin, Peining Zhen, Tianshu Hou, C. Ng, Hai-Bao Chen, Hao Yu, Ngai Wong
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引用次数: 1

Abstract

Real-time instance segmentation is crucial in various AI applications. This work designs a network named Fast Attention based Single-Stage Segmentation NeT (FASSST) that performs instance segmentation with video-grade speed. Using an instance attention module (IAM), FASSST quickly locates target instances and segments with region of interest (ROI) feature fusion (RFF) aggregating ROI features from pyramid mask layers. The module employs an efficient single-stage feature regression, straight from features to instance coordinates and class probabilities. Experiments on COCO and CityScapes datasets show that FASSST achieves state-of-the-art performance under competitive accuracy: real-time inference of 47.5FPS on a GTX1080Ti GPU and 5.3FPS on a Jetson Xavier NX board with only 71.6 GFLOPs.
FASSST:基于快速注意力的单阶段实时实例分割网络
实时实例分割在各种人工智能应用中至关重要。本文设计了一个基于快速注意力的单阶段分割网络(Fast Attention based Single-Stage Segmentation NeT, FASSST),以视频级的速度进行实例分割。FASSST使用实例关注模块(IAM),利用感兴趣区域(ROI)特征融合(RFF)从金字塔掩模层中聚合ROI特征,快速定位目标实例和段。该模块采用高效的单阶段特征回归,直接从特征到实例坐标和类概率。在COCO和cityscape数据集上的实验表明,FASSST在竞争精度下达到了最先进的性能:在GTX1080Ti GPU上的实时推理速度为47.5FPS,在Jetson Xavier NX板上的实时推理速度为5.3FPS,仅为71.6 GFLOPs。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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