Attention Map

Attention maps, visualizations of the weighting process within neural networks, are crucial for understanding model decision-making and improving model performance. Current research focuses on refining attention mechanisms within transformer architectures like Vision Transformers (ViTs) and BERT, addressing issues such as artifact reduction, efficient computation (e.g., through token selection), and improved alignment with input data (e.g., text prompts or object features). This work has implications for diverse fields, including image processing, natural language processing, and even medical diagnostics, by enhancing model interpretability, accuracy, and efficiency. The development of robust and interpretable attention maps is a key area of ongoing investigation.