MEScan360: A Memory-Enhanced Scanpath Prediction Model for Omnidirectional Images

Scanpath prediction for omnidirectional images (ODIs) aims to capture the dynamic human visual attention. However, the complicated gaze behavior and inevitable projection distortion make scanpath prediction in ODIs extremely challenging. Most existing models neither capture the long-term dependencies across visual states nor fully incorporate historical memory information, leading to limited performance. To this end, we propose MEScan360, a memory-enhanced scanpath prediction model for ODIs. We introduce two key innovations: long-term memory storage unit and memory interaction module. These two components establish a more explicit link between past visual information and current visual inputs, thereby significantly enhancing the performance of scanpath prediction. Furthermore, a robust feature extraction module is designed to extract semantic feature precisely from distorted ODIs with a more lightweight structure. Extensive experiments on several benchmark datasets demonstrate that our proposed model achieves competitive performance in both accuracy and efficiency.