Context Aware Transformer

Context-aware transformers enhance the standard transformer architecture by incorporating contextual information to improve model performance and adaptability across diverse tasks. Current research focuses on integrating contextual information through various mechanisms, including specialized attention modules, gated residual connections, and dual-branch architectures that process both local and global features. This approach leads to significant improvements in accuracy and robustness for applications ranging from image retrieval and medical image analysis to speech recognition and visual search, demonstrating the power of context-sensitive processing in deep learning models.