Unimodal Classifier

Unimodal classifiers focus on extracting information and making predictions from a single data modality (e.g., text, images, or audio), aiming to achieve high accuracy and efficiency. Current research explores various architectures, including Transformers, LSTMs, and novel aggregation methods, to improve feature representation and handle challenges like missing data or noisy inputs, often within the context of multimodal systems where unimodal components are integrated. These advancements are significant for applications ranging from speech recognition and sentiment analysis to robust face classification and domain adaptation, improving the performance and efficiency of various machine learning tasks.