Modality Discrepancy

Modality discrepancy, the mismatch in information between different data types (e.g., text and images), is a central challenge in multimodal learning. Current research focuses on developing methods to align or bridge these discrepancies, often employing transformer architectures and techniques like contrastive learning, meta-learning, and modality imputation to improve knowledge transfer and cooperation between modalities. Addressing modality discrepancy is crucial for advancing multimodal applications across diverse fields, including medical diagnosis, automated captioning, and person re-identification, where robust performance requires effective integration of heterogeneous data sources. This leads to improved accuracy and reliability in systems that leverage multiple data modalities.