在Curve-GCN中,注释器会选择一个对象,然后选择多边形或样条轮廓。 Curve-GCN自动地勾勒出对象的轮廓: Curve-GCN允许交互式更正,并且可以自动重新预测多边形/样条 与Polygon-RNN + +相比: Curve-GCN具有多边形或样条曲线参数 Curve-GCN可同时预测控制点(更快) 代码: https://github.com/fidler-lab/curve-gcn 论文...
https://github.com/fidler-lab/curve-gcngithub.com/fidler-lab/curve-gcn 摘要 本文提出的新框架,通过使用图卷积网络(GCN)同时预测边界上每个顶点的演变方向。 端到端训练框架和模型; 通过该方法,可实现:画一个多边形,快速完成物体的轮廓标注。 Curve-GCN比现有的方法性能都要好,包括PSP-DeepLab,效率比Polyg...
This is the official PyTorch implementation of Curve-GCN (CVPR 2019). This repository allows you to train new Curve-GCN models. For technical details, please refer to: Fast Interactive Object Annotation with Curve-GCN Huan Ling*1,2,Jun Gao*1,2,Amlan Kar1,2,Wenzheng Chen1,2,Sanja Fidler1...
Curve-GCN This is the official PyTorch implementation of Curve-GCN (CVPR 2019). This repository allows you to train new Curve-GCN models. For technical details, please refer to: Fast Interactive Object Annotation with Curve-GCN Huan Ling*1,2,Jun Gao*1,2,Amlan Kar1,2,Wenzheng Chen1,2,San...
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Curve-GCN是一种高效交互式图像标注方法,其性能优于Polygon-RNN++。在自动模式下运行时间为29.3ms,在交互模式下运行时间为2.6ms,比Polygon-RNN ++分别快10倍和100倍。 数据标注是人工智能产业的基础,在机器的世界里,图像与语音、视频等一样,是数据的一个种类。