Saliency Transformer

Saliency transformers are a class of deep learning models designed to predict visual attention, mimicking how humans prioritize information in images and videos. Current research focuses on improving efficiency, unifying models across diverse image types (e.g., natural scenes, web pages), and enhancing explainability, particularly for applications like autonomous driving. These models leverage transformer architectures, often incorporating techniques like attention aggregation across layers and parallel decoding, to achieve state-of-the-art performance on various benchmarks, impacting fields such as marketing, robotics, and computer vision. Furthermore, research is exploring weakly supervised training methods to reduce reliance on extensive manual annotation.