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

TL;DR

This survey reviews recent advances in vision and language research, highlighting key trends, applications, challenges, and future directions in multimodal deep learning systems.

Contribution

It provides a comprehensive overview of current and emerging trends in vision-language research, emphasizing task-specific developments and interdisciplinary insights.

Findings

Identification of key research trends in VisLang

Analysis of evaluation strategies for multimodal tasks

Discussion of future challenges and directions

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.