Object to Environment Saliency

Object-to-environment saliency research focuses on understanding how visual attention is directed, particularly concerning the relationship between objects and their surrounding context. Current efforts concentrate on improving models that predict gaze allocation, often incorporating task-specific information and leveraging advanced architectures like Vision Transformers and convolutional neural networks to better capture both local and global contextual cues. This work is crucial for enhancing applications such as driver assistance systems, video memorability prediction, and image matting, by providing more accurate and robust models of visual perception.