  We introduce GQA, a new dataset for real-world visual reasoning and
compositional question answering, seeking to address key shortcomings of
previous VQA datasets. We have developed a strong and robust question engine
that leverages scene graph structures to create 22M diverse reasoning
questions, all come with functional programs that represent their semantics. We
use the programs to gain tight control over the answer distribution and present
a new tunable smoothing technique to mitigate question biases. Accompanying the
dataset is a suite of new metrics that evaluate essential qualities such as
consistency, grounding and plausibility. An extensive analysis is performed for
baselines as well as state-of-the-art models, providing fine-grained results
for different question types and topologies. Whereas a blind LSTM obtains mere
42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%,
offering ample opportunity for new research to explore. We strongly hope GQA
will provide an enabling resource for the next generation of models with
enhanced robustness, improved consistency, and deeper semantic understanding
for images and language.
