Attention Flow

Attention flow research focuses on understanding and manipulating the influence of individual input elements on a model's output, particularly within transformer-based architectures. Current work explores efficient methods for computing and visualizing these flows, often leveraging flow network theory to address computational limitations and improve model performance in tasks like video generation and scene graph construction. This research aims to enhance model interpretability, improve efficiency for handling long sequences, and ultimately lead to more robust and accurate AI systems across various domains. The development of novel attention mechanisms, such as flow-attention, demonstrates a significant step towards achieving these goals.