Visual Saliency

Visual saliency research aims to understand and model how humans and machines prioritize visual information, focusing on what attracts attention in a scene. Current research explores this through various computational models, including deep neural networks (like ResNets and Vision Transformers), spiking neural networks, and generative models, often incorporating techniques like mixup and data augmentation to improve performance and generalization. This field is crucial for improving human-computer interaction, enhancing computer vision applications (e.g., autonomous driving, image manipulation detection), and providing insights into the mechanisms of human visual attention.