论文标题
将eigencontours应用于基于polarmask的实例细分
Applying Eigencontours to PolarMask-Based Instance Segmentation
论文作者
论文摘要
Eigencontours是基于单数值分解的第一个数据驱动的轮廓描述符。根据ESE-SEG的实现，将Eigencontours成功地应用于实例细分任务。在本报告中，我们将Eigencontours纳入Polarmask网络，例如分割。实验结果表明，在CoCO2017和SBD的两个实例分割数据集上，所提出的算法比Polarmask产生的结果更好。此外，我们在定性上分析了特征的特征。我们的代码可在https://github.com/dnjs3594/eigencontours上找到。
Eigencontours are the first data-driven contour descriptors based on singular value decomposition. Based on the implementation of ESE-Seg, eigencontours were applied to the instance segmentation task successfully. In this report, we incorporate eigencontours into the PolarMask network for instance segmentation. Experimental results demonstrate that the proposed algorithm yields better results than PolarMask on two instance segmentation datasets of COCO2017 and SBD. Also, we analyze the characteristics of eigencontours qualitatively. Our codes are available at https://github.com/dnjs3594/Eigencontours.
