Text-VQA Aug: Pipelined Harnessing of Large Multimodal Models for Automated Synthesis

TL;DR

This paper introduces an automated pipeline that synthesizes large-scale text-VQA datasets by leveraging multimodal foundation models, OCR, and question generation, significantly reducing manual annotation efforts.

Contribution

It presents the first end-to-end pipeline for automatic synthesis and validation of a large text-VQA dataset using multiple AI components.

Findings

Generated 72K QA pairs from 44K images

Automated validation ensures data quality

Scales efficiently with scene text data

Abstract

Creation of large-scale databases for Visual Question Answering tasks pertaining to the text data in a scene (text-VQA) involves skilful human annotation, which is tedious and challenging. With the advent of foundation models that handle vision and language modalities, and with the maturity of OCR systems, it is the need of the hour to establish an end-to-end pipeline that can synthesize Question-Answer (QA) pairs based on scene-text from a given image. We propose a pipeline for automated synthesis for text-VQA dataset that can produce faithful QA pairs, and which scales up with the availability of scene text data. Our proposed method harnesses the capabilities of multiple models and algorithms involving OCR detection and recognition (text spotting), region of interest (ROI) detection, caption generation, and question generation. These components are streamlined into a cohesive pipeline to automate the synthesis and validation of QA pairs. To the best of our knowledge, this is the first pipeline proposed to automatically synthesize and validate a large-scale text-VQA dataset comprising around 72K QA pairs based on around 44K images.