Modal Embeddings

Modal embeddings represent a crucial area of research focusing on creating unified representations of data from different modalities (e.g., text, images, audio). Current research emphasizes improving the alignment and fusion of these embeddings, often using transformer-based architectures and contrastive learning methods, to address issues like modality gaps and redundancy. This work is significant because effective multimodal embeddings are essential for advancing numerous applications, including improved search systems, more robust anomaly detection, and enhanced zero-shot learning capabilities across various domains.