  We present an empirical study of active learning for Visual Question
Answering, where a deep VQA model selects informative question-image pairs from
a pool and queries an oracle for answers to maximally improve its performance
under a limited query budget. Drawing analogies from human learning, we explore
cramming (entropy), curiosity-driven (expected model change), and goal-driven
(expected error reduction) active learning approaches, and propose a fast and
effective goal-driven active learning scoring function to pick question-image
pairs for deep VQA models under the Bayesian Neural Network framework. We find
that deep VQA models need large amounts of training data before they can start
asking informative questions. But once they do, all three approaches outperform
the random selection baseline and achieve significant query savings. For the
scenario where the model is allowed to ask generic questions about images but
is evaluated only on specific questions (e.g., questions whose answer is either
yes or no), our proposed goal-driven scoring function performs the best.
