VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges
Artificial Intelligence (AI) and its applications have sparked extraordinary
interest in recent years. This achievement can be ascribed in part to advances
in AI subfields including Machine Learning (ML), Computer Vision (CV), and
Natural Language Processing (NLP). Deep learning, a sub-field of machine
learning that employs artificial neural network concepts, has enabled the most
rapid growth in these domains. The integration of vision and language has
sparked a lot of attention as a result of this. The tasks have been created in
such a way that they properly exemplify the concepts of deep learning. In this
review paper, we provide a thorough and an extensive review of the state of the
arts approaches, key models design principles and discuss existing datasets,
methods, their problem formulation and evaluation measures for VQA and Visual
reasoning tasks to understand vision and language representation learning. We
also present some potential future paths in this field of research, with the
hope that our study may generate new ideas and novel approaches to handle
existing difficulties and develop new applications.