Single Modality

Single-modality processing, focusing on analyzing data from a single source (e.g., image, audio, text), is being challenged by the increasing recognition of the benefits of multimodal approaches. Current research emphasizes developing efficient methods for fusing information from multiple modalities, often employing transformer-based architectures and techniques like attention mechanisms and cross-modal transfer learning to improve performance on tasks ranging from image segmentation and quality assessment to medical diagnosis and robotics. This shift towards multimodal analysis is driven by the understanding that combining data from different sources yields more robust and accurate results, leading to significant advancements in various fields.