Joint Representation

Joint representation learning focuses on creating unified, shared representations of data from multiple modalities (e.g., images, text, sensor data) to improve model performance and generalization across diverse tasks. Current research emphasizes efficient model architectures like transformers and graph neural networks, often incorporating contrastive learning or knowledge distillation to align and fuse these multimodal features. This approach is proving valuable in various applications, including improved object recognition, action anticipation, and multimodal understanding, by leveraging complementary information from different data sources to overcome limitations of single-modality approaches.