论文标题
开放式语义细分的有条件重建
Conditional Reconstruction for Open-set Semantic Segmentation
论文作者
论文摘要
开放式分割是一种相对较新且未探索的任务，仅提出了少数方法来对SUCHTASK进行建模。我们提出了一种新的方法，称为Coreseg Thatackles，使用类似的输入图像根据其Pixelwise Mask对输入图像进行条件重建。我们的方法将每个输入像素对所有已知类别的输入像素都预期，预期未知类别的像素的错误更高。 ITWA观察到，所提出的方法在其预测中产生更好的Se-Mantic一致性，从而产生了更拟合物体边界的清洁式化图。 Core-seg在Vaihin-Gen和Potsdam ISPRS数据集上优于最先进的方法，同时在休斯顿2018 IEEE GRSS DATA FUSIONDATASET上也是prect的。 Coreseg的官方实施可用：https：//github.com/iannunes/coreseg。
Open set segmentation is a relatively new and unexploredtask, with just a handful of methods proposed to model suchtasks.We propose a novel method called CoReSeg thattackles the issue using class conditional reconstruction ofthe input images according to their pixelwise mask. Ourmethod conditions each input pixel to all known classes,expecting higher errors for pixels of unknown classes. Itwas observed that the proposed method produces better se-mantic consistency in its predictions, resulting in cleanersegmentation maps that better fit object boundaries. CoRe-Seg outperforms state-of-the-art methods on the Vaihin-gen and Potsdam ISPRS datasets, while also being com-petitive on the Houston 2018 IEEE GRSS Data Fusiondataset. Official implementation for CoReSeg is availableat:https://github.com/iannunes/CoReSeg.
