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The multimodal task of Visual Question Answering (VQA) encompassing elementsof Computer Vision (CV) and Natural Language Processing (NLP), aims to generateanswers to questions on any visual input. Over time, the scope of VQA hasexpanded from datasets focusing on an extensive collection of natural images todatasets featuring synthetic images, video, 3D environments, and various othervisual inputs. The emergence of large pre-trained networks has shifted theearly VQA approaches relying on feature extraction and fusion schemes to visionlanguage pre-training (VLP) techniques. However, there is a lack ofcomprehensive surveys that encompass both traditional VQA architectures andcontemporary VLP-based methods. Furthermore, the VLP challenges in the lens ofVQA haven't been thoroughly explored, leaving room for potential open problemsto emerge. Our work presents a survey in the domain of VQA that delves into theintricacies of VQA datasets and methods over the field's history, introduces adetailed taxonomy to categorize the facets of VQA, and highlights the recenttrends, challenges, and scopes for improvement. We further generalize VQA tomultimodal question answering, explore tasks related to VQA, and present a setof open problems for future investigation. The work aims to navigate bothbeginners and experts by shedding light on the potential avenues of researchand expanding the boundaries of the field.

From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities