Multi modEl Fusion

Multi-model fusion aims to improve the performance and robustness of machine learning systems by combining information from multiple models or modalities (e.g., text, images, audio). Current research focuses on developing efficient fusion techniques, including adversarial learning to encourage complementary representations and adaptive methods to handle model heterogeneity, often employing transformer architectures and various deep learning approaches like YOLO and LSTM networks. This field is significant because it addresses limitations of single-model systems across diverse applications, from speaker verification and image classification to federated learning and video object segmentation, leading to more accurate and reliable results.