  The multimodal task of Visual Question Answering (VQA) encompassing elements
of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate
answers to questions on any visual input. Over time, the scope of VQA has
expanded from datasets focusing on an extensive collection of natural images to
datasets featuring synthetic images, video, 3D environments, and various other
visual inputs. The emergence of large pre-trained networks has shifted the
early VQA approaches relying on feature extraction and fusion schemes to vision
language pre-training (VLP) techniques. However, there is a lack of
comprehensive surveys that encompass both traditional VQA architectures and
contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of
VQA haven't been thoroughly explored, leaving room for potential open problems
to emerge. Our work presents a survey in the domain of VQA that delves into the
intricacies of VQA datasets and methods over the field's history, introduces a
detailed taxonomy to categorize the facets of VQA, and highlights the recent
trends, challenges, and scopes for improvement. We further generalize VQA to
multimodal question answering, explore tasks related to VQA, and present a set
of open problems for future investigation. The work aims to navigate both
beginners and experts by shedding light on the potential avenues of research
and expanding the boundaries of the field.
