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Despite their importance in training artificial intelligence systems, largedatasets remain challenging to acquire. For example, the ImageNet datasetrequired fourteen million labels of basic human knowledge, such as whether animage contains a chair. Unfortunately, this knowledge is so simple that it istedious for human annotators but also tacit enough such that they arenecessary. However, human collaborative efforts for tasks like labeling massiveamounts of data are costly, inconsistent, and prone to failure, and this methoddoes not resolve the issue of the resulting dataset being static in nature.What if we asked people questions they want to answer and collected theirresponses as data? This would mean we could gather data at a much lower cost,and expanding a dataset would simply become a matter of asking more questions.We focus on the task of Visual Question Answering (VQA) and propose a systemthat uses Visual Question Generation (VQG) to produce questions, asks them tosocial media users, and collects their responses. We present two models thatcan then parse clean answers from the noisy human responses significantlybetter than our baselines, with the goal of eventually incorporating theanswers into a Visual Question Answering (VQA) dataset. By demonstrating howour system can collect large amounts of data at little to no cost, we envisionsimilar systems being used to improve performance on other tasks in the future.

A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop