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Abstract
Multimodal data fusion combines information from multiple modalities, such as text and images, to achieve a richer representation for natural language processing (NLP) and computer vision (CV) tasks. Deep learning architectures have become a cornerstone for such fusion tasks due to their ability to capture complex patterns and interactions. This paper explores prominent deep learning models employed for multimodal data fusion, including feature concatenation, attention mechanisms, and modality-specific encoders. Additionally, we discuss the challenges in integrating heterogeneous data sources, addressing issues such as modality imbalance and information alignment. The findings highlight the evolution of multimodal architectures, emphasizing their significance in advancing tasks such as visual question answering, image captioning, and text-to-image synthesis.
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