Multimodal Fusion

Multimodal fusion refers to the integration of information from multiple modalities, such as text, images, and audio, to create a unified representation that enhances understanding and decision-making. This process often involves feature extraction from each modality, followed by techniques such as early fusion, late fusion, or hybrid approaches to combine the features. Early fusion integrates raw data or features at the input level, while late fusion combines the outputs of separate models trained on each modality. The effectiveness of multimodal fusion can be evaluated using metrics such as accuracy, precision, and recall, depending on the specific task. Recent advancements in deep learning, particularly with transformer architectures, have facilitated more effective multimodal representations, enabling applications in areas such as video analysis, sentiment detection, and human-computer interaction.