An iterative representation learning framework to predict the sequence of eye fixations
Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath. Within the proposed framework, saccade can be explained as an iterative process of finding the most uncertain area and updating the representation of scenes. In implementation, a deep autoencoder is employed for representation learning. The current fixation is predicted to be the most salient pixel, with saliency estimated by the reconstruction residual of the deep network. Image patches around this fixation are then sampled to update the network for the selection of subsequent fixations. Compared with existing models, the proposed model shows the state-of-the-art performance on several public data sets.