An Overview Recent Trends and Challenges in Multi-Modal Image Retrieval Using Deep Learning

Recent years we have seen a major increase in interest in multi-modal image retrieval, which includes looking for and getting pictures based on many modalities including visual, textual, and audio information. This review paper gives a general overview of current trends and challenges in multimodal image retrieval, with an emphasis on the developments made possible by deep learning methods. We examine several methods, designs, and datasets used in multi-modal image retrieval, highlighting the potential advantages and emerging challenges in this area. This paper offers a thorough examination of present innovations and challenges in multimodal image retrieval, with an emphasis on the developments made possible by deep learning methods, evaluation metrics, architectures, and issues related to multi-modal image retrieval. We also provide a comparative analysis in tabular form to show the benefits and drawbacks of various approaches.