Vision meets language: Multimodal transformers elevating predictive power in visual question answering

Abstract

Visual Question Answering (VQA) is a field where computer vision and natural language processing intersect to develop systems capable of comprehending visual information and answering natural language questions. In visual question answering , algorithms interpret real-world images in response to questions expressed in human language. Our paper presents an extensive experimental study on Visual Question Answering (VQA) using a diverse set of multimodal transformers. The VQA task requires systems to comprehend both visual content and natural language questions. To address this challenge, we explore the performance of various pre-trained transformer architectures for encoding questions, including BERT, RoBERTa, and ALBERT, as well as image transformers, such as ViT, DeiT, and BEiT, for encoding images. Multimodal transformers' smooth fusion of visual and text data promotes cross-modal understanding and strengthens reasoning skills. On benchmark datasets like the Visual Question Answering (VQA) v2.0 dataset, we rigorously test and fine-tune these models to assess their effectiveness and compare their performance to more conventional VQA methods. The results show that multimodal transformers significantly outperform traditional techniques in terms of performance. Additionally, the models' attention maps give users insights into how they make decisions, improving interpretability and comprehension. Because of their adaptability, the tested transformer topologies have the potential to be used in a wide range of VQA applications, such as robotics, healthcare, and assistive technology. This study demonstrates the effectiveness and promise of multimodal transformers as a method for improving the effectiveness of visual question-answering systems.