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We tackle object category discovery, which is the problem of discovering andlocalizing novel objects in a large unlabeled dataset. While existing methodsshow results on datasets with less cluttered scenes and fewer object instancesper image, we present our results on the challenging COCO dataset. Moreover, weargue that, rather than discovering new categories from scratch, discoveryalgorithms can benefit from identifying what is already known and focusingtheir attention on the unknown. We propose a method that exploits priorknowledge about certain object types to discover new categories by leveragingtwo memory modules, namely Working and Semantic memory. We show the performanceof our detector on the COCO minival dataset to demonstrate its in-the-wildcapabilities.

The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory