Visual attention methods in deep learning: An in-depth survey

Mohammed Hassanin, Saeed Anwar, Ibrahim Radwan, Fahad Shahbaz Khan, Ajmal Mian

Abstract: Highlights•An in-depth exploration of attention techniques for gaps, context, and insights.•Categorizing mechanisms for models to distinguish relevant information.•Responding to the attention-related paper surge, guiding adoption in vision.•Diverging from transformer-centric surveys, offering a unique vision perspective.•Providing valuable recommendations for navigating challenges and future direction.