  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.
