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Artificial Intelligence (AI) and its applications have sparked extraordinaryinterest in recent years. This achievement can be ascribed in part to advancesin AI subfields including Machine Learning (ML), Computer Vision (CV), andNatural Language Processing (NLP). Deep learning, a sub-field of machinelearning that employs artificial neural network concepts, has enabled the mostrapid growth in these domains. The integration of vision and language hassparked a lot of attention as a result of this. The tasks have been created insuch a way that they properly exemplify the concepts of deep learning. In thisreview paper, we provide a thorough and an extensive review of the state of thearts approaches, key models design principles and discuss existing datasets,methods, their problem formulation and evaluation measures for VQA and Visualreasoning tasks to understand vision and language representation learning. Wealso present some potential future paths in this field of research, with thehope that our study may generate new ideas and novel approaches to handleexisting difficulties and develop new applications.

VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges