External Knowledge Augmented Text Visual Question Answering

• URL: http://arxiv.org/abs/2108.09717v1
• Date: Sun, 22 Aug 2021 13:21:58 GMT
• Title: External Knowledge Augmented Text Visual Question Answering
• Authors: Arka Ujjal Dey, Ernest Valveny, Gaurav Harit
• Abstract summary: We propose a framework to extract, filter, and encode knowledge atop a standard multimodal transformer for vision language understanding tasks. We generate results comparable to the state-of-the-art on two publicly available datasets.
• Score: 0.6445605125467573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The open-ended question answering task of Text-VQA requires reading and reasoning about local, often previously unseen, scene-text content of an image to generate answers. In this work, we propose the generalized use of external knowledge to augment our understanding of the said scene-text. We design a framework to extract, filter, and encode knowledge atop a standard multimodal transformer for vision language understanding tasks. Through empirical evidence, we demonstrate how knowledge can highlight instance-only cues and thus help deal with training data bias, improve answer entity type correctness, and detect multiword named entities. We generate results comparable to the state-of-the-art on two publicly available datasets, under the constraints of similar upstream OCR systems and training data.

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