Joint Embedding

Joint embedding techniques aim to create unified representations of data from multiple modalities (e.g., images, text, audio) within a shared vector space, enabling cross-modal comparisons and downstream tasks like retrieval and classification. Current research focuses on optimizing embedding algorithms (e.g., contrastive learning, optimal transport) and architectures (e.g., transformers, graph neural networks) to improve the quality and alignment of these joint embeddings, particularly addressing challenges like class imbalance and hubness. This field is significant for its potential to unlock insights from diverse data sources and improve applications ranging from anomaly detection in infrastructure inspection to more accurate house price prediction and enhanced multi-object tracking.