Existing question answering datasets mostly contain homogeneous contexts, based on either textual or visual information alone. On the other hand, digitalization has evolved the nature of reading which often includes integrating information across multiple heterogeneous sources. To bridge the gap between two, we compile a Diverse Visuo-Lingustic Question Answering (DVLQA) challenge corpus, where the task is to derive joint inference about the given image-text modality  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 of them would make the question unanswerable. We first explore the combination of best existing deep learning architectures for visual question answering and machine comprehension to solve DVLQA subsets and show that they are unable to reason well on the joint task . We then develop a modular method which demonstrates slightly better baseline performance and offers more transparency for interpretation of intermediate outputs. However, this is still far behind the human performance, therefore we believe DVLQA will be a challenging benchmark for question answering involving reasoning over visuo-linguistic context. The dataset, code and public leaderboard will be made available at URL
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 URL
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Existing question answering datasets mostly contain homogeneous contexts, based on either textual or visual information alone. On the other hand, digitalization has evolved the nature of reading which often includes integrating information across multiple heterogeneous sources. To bridge the gap between two, we compile a Diverse Visuo-Lingustic Question Answering (DVLQA) challenge corpus, where the task is
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
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