Single Modal

Single-modal analysis, focusing on extracting information from a single data source (e.g., image, text, or audio), faces limitations in accuracy and completeness compared to multimodal approaches. Current research emphasizes improving single-modal performance by leveraging techniques from multimodal learning, such as knowledge transfer from pre-trained multimodal models or incorporating pseudo-modalities to mimic the benefits of multi-sensor data. This involves adapting architectures like vision transformers and employing novel loss functions to optimize single-modal classifiers. These advancements are crucial for applications where acquiring multiple data modalities is impractical or expensive, improving efficiency and accuracy in diverse fields like medical diagnosis and information verification.