论文标题
通过向基于知识的新颖对象识别提出问题来学习
Learning by Asking Questions for Knowledge-based Novel Object Recognition
论文作者
论文摘要
在现实世界识别中，需要识别许多对象类。基于监督学习的常规图像识别只能识别培训数据中存在的对象类，因此在现实世界中的适用性有限。另一方面，人类可以通过提出问题并获取有关它们的知识来识别新颖的对象。受此启发的启发，我们研究了一个通过问题产生获取外部知识的框架，该框架将有助于该模型立即识别新颖的对象。我们的管道由两个组成部分组成：对象分类器，它执行基于知识的对象识别，以及生成知识意识的问题以获取新知识的问题。我们还根据对象分类器的知识意识预测的信心提出了一个问题生成策略。为了训练问题生成器，我们构建了一个数据集，其中包含有关图像中对象的知识知识问题。我们的实验表明，与几个基线相比，提出的管道有效地获得了有关新物体的知识。
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.
