Visual question answering: from early developments to recent advances -- a survey

TL;DR

This survey reviews the evolution of Visual Question Answering (VQA), covering architectures, datasets, and applications, emphasizing recent advances like Large Visual Language Models and outlining future research challenges.

Contribution

It provides a comprehensive taxonomy of VQA architectures, reviews deep learning methods and emerging LVLMs, and discusses datasets, evaluation metrics, and future research directions.

Findings

Deep learning-based VQA approaches dominate the field.

Large Visual Language Models show promising results in VQA tasks.

Identified key challenges and open questions for future research.

Abstract

Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techniques such as feature extraction, object detection, text embedding, natural language understanding, and language generation. With the growth of multimodal data research, VQA has gained significant attention due to its broad applications, including interactive educational tools, medical image diagnosis, customer service, entertainment, and social media captioning. Additionally, VQA plays a vital role in assisting visually impaired individuals by generating descriptive content from images. This survey introduces a taxonomy of VQA architectures, categorizing them based on design choices and key components to facilitate comparative analysis and evaluation. We review major VQA approaches, focusing on deep learning-based methods, and explore the emerging field of Large Visual Language Models (LVLMs) that have demonstrated success in multimodal tasks like VQA. The paper further examines available datasets and evaluation metrics essential for measuring VQA system performance, followed by an exploration of real-world VQA applications. Finally, we highlight ongoing challenges and future directions in VQA research, presenting open questions and potential areas for further development. This survey serves as a comprehensive resource for researchers and practitioners interested in the latest advancements and future