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

• URL: http://arxiv.org/abs/2105.01652v2
• Date: Wed, 5 May 2021 01:23:56 GMT
• Title: The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
• Authors: Sai Saketh Rambhatla and Rama Chellappa and Abhinav Shrivastava
• Abstract summary: We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset. We propose a method to use prior knowledge about certain object categories to discover new categories by leveraging two memory modules. We show the performance of our detector on the COCO minival dataset to demonstrate its in-the-wild capabilities.
• Score: 85.01439251151203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 to use prior knowledge about certain object categories 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.

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