We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and the learner must ask questions it wants answers to. Thus, LBA more closely mimics natural learning and has the potential to be more data-efficient than the traditional VQA setting. We present a model that performs LBA on the CLEVR dataset, and show that it automatically discovers an easy-to-hard curriculum when learning interactively from an oracle. Our LBA generated data consistently matches or outperforms the CLEVR train data and is more sample efficient. We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions.

本文介绍了一种基于“学问即问”（LBA）的交互式学习框架用于开发和测试智能视觉系统在视觉问题回答方面的应用， 不同于标准的VQA 训练，LBA更接近自然学习且可能具有更高的数据效率，自动生成的数据在CLEVR数据集上具有很好的性能和表现，对最新的VQA模型和新的测试数据分布具有很好的推广与实现。

问答式学习