Multimodal

Multimodal refers to systems, methods, or research that integrate information from more than one modality. In information processing, modalities include text, speech, vision (images and video), audio, touch, and other sensory or sensor-derived data. The goal is to improve perception, understanding, and interaction by exploiting the complementary information provided by different signals. In artificial intelligence, multimodal learning seeks representations and models that capture relationships across modalities and can operate when one or more modalities are noisy or missing.

Modalities and representative examples: Visual data such as images or video; auditory data including speech and ambient sounds; textual data; tactile or haptic data; physiological or sensor signals. Multimodal systems often seek to align and fuse signals that may arrive asynchronously or at different rates.

Applications: Multimodal interfaces that combine speech and gesture; multimedia information retrieval and content understanding; image or video captioning and Visual Question Answering; cross-modal retrieval; assistive technology; robotics and autonomous systems; healthcare, where imaging, clinical notes, and sensor data may be jointly analyzed.

Approaches: Fusion strategies are typically categorized as early (feature-level) fusion, late (decision-level) fusion, or joint embedding with cross-modal attention. Modern methods frequently use transformer-based architectures to model cross-modal interactions. Common tasks include multimodal sentiment analysis, audiovisual speech recognition, and cross-modal retrieval. Datasets such as COCO, Visual Genome, and other multimodal benchmarks drive progress.

Challenges: Synchronization and alignment across modalities, handling missing data, differing data quality and sampling rates, scaling to high-dimensional inputs, computational complexity, and fairness or privacy concerns. Evaluation can be difficult because success depends on the specific application and the interplay among modalities.

See also: Multimodal learning, multisensory integration, sensor fusion, cross-modal retrieval.