  We introduce the task of Image-Set Visual Question Answering (ISVQA), which
generalizes the commonly studied single-image VQA problem to multi-image
settings. Taking a natural language question and a set of images as input, it
aims to answer the question based on the content of the images. The questions
can be about objects and relationships in one or more images or about the
entire scene depicted by the image set. To enable research in this new topic,
we introduce two ISVQA datasets - indoor and outdoor scenes. They simulate the
real-world scenarios of indoor image collections and multiple car-mounted
cameras, respectively. The indoor-scene dataset contains 91,479 human annotated
questions for 48,138 image sets, and the outdoor-scene dataset has 49,617
questions for 12,746 image sets. We analyze the properties of the two datasets,
including question-and-answer distributions, types of questions, biases in
dataset, and question-image dependencies. We also build new baseline models to
investigate new research challenges in ISVQA.
