Understanding images and text together is an important aspect of cognition
and building advanced Artificial Intelligence (AI) systems. As a community, we
have achieved good benchmarks over language and vision domains separately,
however joint reasoning is still a challenge for state-of-the-art computer
vision and natural language processing (NLP) systems. We propose a novel task
to derive joint inference about a given image-text modality and compile the
Visuo-Linguistic Question Answering (VLQA) challenge corpus in a question
answering setting. Each dataset item consists of an image and a reading
passage, where questions are designed to combine both visual and textual
information i.e., ignoring either modality would make the question
unanswerable. We first explore the best existing vision-language architectures
to solve VLQA subsets and show that they are unable to reason well. We then
develop a modular method with slightly better baseline performance, but it is
still far behind human performance. We believe that VLQA will be a good
benchmark for reasoning over a visuo-linguistic context. The dataset, code and
leaderboard is available at this https URL

新方法和基础性性能的比较与现有机器学习方法中挑战的问题进行了讨论，最终提出了 Visuo-Linguistic Question Answering 作为计算机视觉和自然语言处理的基准测试集。