Multimodal Classification

Multimodal classification aims to improve the accuracy and robustness of classification tasks by integrating information from multiple data sources (e.g., images, text, sensor data). Current research emphasizes developing effective fusion strategies, often employing transformer-based architectures or contrastive learning methods, to combine these diverse modalities and address challenges like missing data and modality imbalance. This field is significant for its potential to enhance various applications, including medical diagnosis, social media analysis, and robotics, by leveraging the complementary strengths of different data types. Improved methods are also focusing on addressing issues of confidence calibration and balanced learning across modalities.