Elevating Textual Question Answering with On-Demand Visual Augmentation

Textual Question Answering (TQA) remains a formidable challenge, despite over a decade of research. The integration of transformer networks and external knowledge via pre-trained models has marked a significant advancement in TQA. Yet, a crucial element often overlooked is the incorporation of external visual understanding. In this study, we introduce an innovative TQA approach that equips machines with the capability for on-demand visual grounding, thereby enriching their comprehension of questions and enhancing the relevance of generated answers. Our methodology utilizes web image search to tap into a vast pool of global knowledge and employs a novel technique for determining the most appropriate answer through on-demand visual grounding. We present a variety of multimedia model configurations, showcasing that our proposed method not only surpasses existing systems without necessitating pre-training but also achieves performance comparable to fine-tuned models 30 times its size as well as closed-source LLMs such as GPT-4o, a testament to its efficiency. Furthermore, an interpretability analysis reveals the integral role of visual grounding in the model's decision-making process. This research offers a fresh outlook on augmenting TQA performance by harnessing the potential of visual grounding, with broad implications for natural language processing and artificial intelligence.