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.

中文翻译：

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知识的追求：使用双记忆技术发现和本地化新类别

我们处理对象类别发现，这是在大型未标记数据集中发现和定位新颖对象的问题。尽管现有方法在具有较少杂乱场景和较少图像的对象实例的数据集上显示结果，但我们在具有挑战性的COCO数据集上展示了我们的结果。此外，我们认为，发现算法可以从识别已知的内容并将注意力集中在未知的内容上，而不是从头开始发现新的类别。我们提出一种方法，利用有关某些对象类别的先验知识，通过利用两个内存模块（即工作内存和语义内存）来发现新​​类别。我们在COCO minival数据集上显示了检测器的性能，以证明其在野外的功能。