Multimodal Research in Vision and Language: A Review of Current and Emerging Trends
It is a review paper that synthesizes existing literature to guide researchers and practitioners in understanding and advancing multimodal AI systems.
This paper provides a detailed overview of current and emerging trends in multimodal vision and language research, identifying key applications, evaluation strategies, and future directions for the field.
Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhanced research interests in the intersection of the Vision and Language arena with its numerous applications and fast-paced growth. In this paper, we present a detailed overview of the latest trends in research pertaining to visual and language modalities. We look at its applications in their task formulations and how to solve various problems related to semantic perception and content generation. We also address task-specific trends, along with their evaluation strategies and upcoming challenges. Moreover, we shed some light on multi-disciplinary patterns and insights that have emerged in the recent past, directing this field towards more modular and transparent intelligent systems. This survey identifies key trends gravitating recent literature in VisLang research and attempts to unearth directions that the field is heading towards.