What is the difference in robustness scores between ViLT models trained with SMOTE versus StyleGAN-augmented d

Abstract

Abstract: 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

Research Question

What is the difference in robustness scores between ViLT models trained with SMOTE versus StyleGAN-augmented datasets when evaluated on adversarial VQA benchmarks?

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