Text-VQA Aug: Pipelined Harnessing of Large Multimodal Models for Automated Synthesis
This addresses the tedious and challenging human annotation process for text-VQA tasks, offering a scalable solution for dataset creation.
The paper tackles the problem of creating large-scale text-VQA datasets by proposing an automated pipeline that synthesizes and validates QA pairs from scene-text images, resulting in a dataset of around 72K QA pairs from 44K images.
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.