EKTVQA: Generalized Use Of External Knowledge To Empower Scene Text In Text-vqa

Dey Arka Ujjal, Valveny Ernest, Harit Gaurav. IEEE.ACCESS 2021

[Paper]    
Applications Ethics And Bias Model Architecture Multimodal Models Pretraining Methods Tools Training Techniques Transformer

The open-ended question answering task of Text-VQA often requires reading and reasoning about rarely seen or completely unseen scene-text content of an image. We address this zero-shot nature of the problem by proposing the generalized use of external knowledge to augment our understanding of the scene text. We design a framework to extract, validate, and reason with knowledge using a standard multimodal transformer for vision language understanding tasks. Through empirical evidence and qualitative results, we demonstrate how external 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 three publicly available datasets, under the constraints of similar upstream OCR systems and training data.

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