  For stability and reliability of real-world applications, the robustness of
DNNs in unimodal tasks has been evaluated. However, few studies consider
abnormal situations that a visual question answering (VQA) model might
encounter at test time after deployment in the real-world. In this study, we
evaluate the robustness of state-of-the-art VQA models to five different
anomalies, including worst-case scenarios, the most frequent scenarios, and the
current limitation of VQA models. Different from the results in unimodal tasks,
the maximum confidence of answers in VQA models cannot detect anomalous inputs,
and post-training of the outputs, such as outlier exposure, is ineffective for
VQA models. Thus, we propose an attention-based method, which uses confidence
of reasoning between input images and questions and shows much more promising
results than the previous methods in unimodal tasks. In addition, we show that
a maximum entropy regularization of attention networks can significantly
improve the attention-based anomaly detection of the VQA models. Thanks to the
simplicity, attention-based anomaly detection and the regularization are
model-agnostic methods, which can be used for various cross-modal attentions in
the state-of-the-art VQA models. The results imply that cross-modal attention
in VQA is important to improve not only VQA accuracy, but also the robustness
to various anomalies.
