  In real-world object recognition, there are numerous object classes to be
recognized. Conventional image recognition based on supervised learning can
only recognize object classes that exist in the training data, and thus has
limited applicability in the real world. On the other hand, humans can
recognize novel objects by asking questions and acquiring knowledge about them.
Inspired by this, we study a framework for acquiring external knowledge through
question generation that would help the model instantly recognize novel
objects. Our pipeline consists of two components: the Object Classifier, which
performs knowledge-based object recognition, and the Question Generator, which
generates knowledge-aware questions to acquire novel knowledge. We also propose
a question generation strategy based on the confidence of the knowledge-aware
prediction of the Object Classifier. To train the Question Generator, we
construct a dataset that contains knowledge-aware questions about objects in
the images. Our experiments show that the proposed pipeline effectively
acquires knowledge about novel objects compared to several baselines.
