Attention Mask

Attention masks are mechanisms used in transformer-based models to selectively focus on relevant information within input sequences, improving efficiency and performance. Current research focuses on optimizing attention mask design for various applications, including long-sequence processing (e.g., through sparse representations), zero-shot video editing (e.g., by dynamically selecting optimal masks), and multimodal tasks (e.g., by integrating mask information with visual and textual data). These advancements enhance model efficiency, accuracy, and interpretability across diverse fields like natural language processing, computer vision, and medical image analysis, leading to improved performance in tasks ranging from image segmentation to language-driven robotic control.