Enhanced Visual Question Answering: A Comparative Analysis and Textual Feature Extraction Via Convolutions

arXiv:2405.00479v1 Announce Type: new
Abstract: Visual Question Answering (VQA) has emerged as a highly engaging field in recent years, attracting increasing research efforts aiming to enhance VQA accuracy through the deployment of advanced models such as Transformers. Despite this growing interest, there has been limited exploration into the comparative analysis and impact of textual modalities within VQA, particularly in terms of model complexity and its effect on performance. In this work, we conduct a comprehensive comparison between complex textual models …

abstract accuracy advanced analysis arxiv comparative analysis cs.cv deployment exploration extraction feature feature extraction question question answering research textual through transformers type via visual vqa