Tensor Fusion

Tensor fusion is a machine learning technique that integrates data from multiple sources (e.g., images, text, audio) to improve model performance and interpretability. Current research focuses on developing efficient fusion methods, often employing neural network architectures like transformers and employing tensor decomposition techniques to handle high-dimensional data, addressing challenges such as heterogeneity among data modalities and optimizing for both accuracy and interpretability. This approach has significant implications across diverse fields, enhancing applications ranging from medical image analysis (e.g., wound segmentation, dementia detection) to student emotion recognition and person re-identification by leveraging the combined power of multimodal data.