  The ideal form of Visual Question Answering requires understanding, grounding
and reasoning in the joint space of vision and language and serves as a proxy
for the AI task of scene understanding. However, most existing VQA benchmarks
are limited to just picking the answer from a pre-defined set of options and
lack attention to text. We present a new challenge with a dataset that contains
23,781 questions based on 10124 image-text pairs. Specifically, the task
requires the model to align multimedia representations of the same entity to
implement multi-hop reasoning between image and text and finally use natural
language to answer the question. The aim of this challenge is to develop and
benchmark models that are capable of multimedia entity alignment, multi-step
reasoning and open-ended answer generation.
