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Analysis Study Research Paper | Computer Science & Engineering | India | Volume 12 Issue 9, September 2023 | Popularity: 4.9 / 10

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Multi-Modal Fusion Techniques in Deep Learning

Radhika Shetty D S

Abstract: Multi-modal fusion techniques in deep learning have gained significant attention due to their capacity to leverage information from diverse sources and enhance the performance of various machine learning applications. This paper provides an overview of the key approaches and strategies employed in the fusion of data from multiple modalities, including images, text, audio, and more. We explore the spectrum of fusion techniques, ranging from early fusion, which combines raw features at the input level, to late fusion, which aggregates predictions at the output level. Additionally, we delve into mid-level fusion techniques that merge representations at intermediate layers within neural networks [1]. Attention mechanisms, such as self-attention and cross-modal attention, play a pivotal role in dynamically weighing the contributions of different modalities during processing. Cross-modal embeddings are discussed as a means to map data from disparate modalities into a shared embedding space, facilitating seamless integration. Graph-based fusion models are explored for their ability to capture inter-modal relationships in a structured manner, while co-attention and co-guidance mechanisms enhance the modeling of interactions between modalities [1]. Hybrid models, combining elements of both early and late fusion, are presented as versatile solutions adaptable to a variety of multi-modal tasks. Memory-augmented neural networks are also examined, offering the capacity to store and retrieve information from different modalities as needed. Through a comprehensive exploration of these multi-modal fusion techniques, this paper aims to provide researchers and practitioners with insights into the advancements and possibilities in the field. These techniques have widespread applications across domains such as natural language processing, computer vision, audio analysis, and beyond, making them a valuable area of study in contemporary deep learning research.

Keywords: Multi-modal fusion techniques, Deep learning, Data fusion, Cross-modal attention, Hybrid models

Edition: Volume 12 Issue 9, September 2023

Pages: 526 - 532

DOI: https://www.doi.org/10.21275/SR23905100554

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