  We tackle object category discovery, which is the problem of discovering and
localizing novel objects in a large unlabeled dataset. While existing methods
show results on datasets with less cluttered scenes and fewer object instances
per image, we present our results on the challenging COCO dataset. Moreover, we
argue that, rather than discovering new categories from scratch, discovery
algorithms can benefit from identifying what is already known and focusing
their attention on the unknown. We propose a method that exploits prior
knowledge about certain object types to discover new categories by leveraging
two memory modules, namely Working and Semantic memory. We show the performance
of our detector on the COCO minival dataset to demonstrate its in-the-wild
capabilities.
