  Visual Question Answering (VQA) entails answering questions about images. We
introduce the first VQA dataset in which all contents originate from an
authentic use case. Sourced from online question answering community forums, we
call it VQAonline. We characterize this dataset and how it relates to eight
mainstream VQA datasets. Observing that answers in our dataset tend to be much
longer (i.e., a mean of 173 words) and so incompatible with standard VQA
evaluation metrics, we instead utilize popular metrics for longer text
evaluation for evaluating six state-of-the-art VQA models on VQAonline and
report where they struggle most. Finally, we analyze which evaluation metrics
align best with human judgments. To facilitate future extensions, we
publicly-share the dataset at: https://vqaonline.github.io/.
