  Benefiting from large-scale pre-training, we have witnessed significant
performance boost on the popular Visual Question Answering (VQA) task. Despite
rapid progress, it remains unclear whether these state-of-the-art (SOTA) models
are robust when encountering examples in the wild. To study this, we introduce
Adversarial VQA, a new large-scale VQA benchmark, collected iteratively via an
adversarial human-and-model-in-the-loop procedure. Through this new benchmark,
we discover several interesting findings. (i) Surprisingly, we find that during
dataset collection, non-expert annotators can easily attack SOTA VQA models
successfully. (ii) Both large-scale pre-trained models and adversarial training
methods achieve far worse performance on the new benchmark than over standard
VQA v2 dataset, revealing the fragility of these models while demonstrating the
effectiveness of our adversarial dataset. (iii) When used for data
augmentation, our dataset can effectively boost model performance on other
robust VQA benchmarks. We hope our Adversarial VQA dataset can shed new light
on robustness study in the community and serve as a valuable benchmark for
future work.
