Title: BinaryVQA: A Versatile Dataset to Push the Limits of VQA Models. Abstract: We introduce a new test set for visual question answering (VQA) called BinaryVQA
to push the limits of VQA models. Our dataset includes 7,800 questions across
1,024 images and covers a wide variety of objects, topics, and concepts. For easy
model evaluation, we only consider binary questions. Questions and answers are
formulated and verified carefully and manually. Around 63% of the questions
have positive answers. The median number of questions per image and question
length are 7 and 5, respectively. The state of the art OFA model achieves 75%
accuracy on BinaryVQA dataset, which is significantly lower than its performance
on the VQA v2 test-dev dataset (94.7%). We also analyze the model behavior along
several dimensions including a) performance over different categories such as text,
counting and gaze direction, b) model interpretability, c) the effect of question
length on accuracy, d) bias of models towards positive answers and introduction of
a new score called the “ShuffleAcc”, and e) sensitivity to spelling and grammar
errors. Our investigation demonstrates the difficulty of our dataset and shows that it
can challenge VQA models for years to come. Data and code is available [Masked].