Multimodal Research in Vision and Language: A Review of Current and Emerging Trends

Abstract

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.

@article{Uppal2022MultimodalRI,
  title={Multimodal research in vision and language: A review of current and emerging trends},
  author={Shagun Uppal and Sarthak Bhagat and Devamanyu Hazarika and Navonil Majumder and Soujanya Poria and Roger Zimmermann and Amir Zadeh},
  journal={Inf. Fusion},
  year={2022},
  volume={77},
  pages={149-171}
}